Final report 06EO · le Nord. Enfin, on a examiné l’agencement des sources nordiques pour tirer...

Final report 06EOSituational Information for Enabling Development of Northern Awareness (SEDNA)
Anthony W. Isenor Anna Liesa S. Lapinski DRDC – Atlantic Research Centre Prepared for: Director Navy Requirements 2
Defence Research and Development Canada
Scientific Report
June 2015
© Her Majesty the Queen in Right of Canada, as represented by the Minister of National Defence, 2015
© Sa Majesté la Reine (en droit du Canada), telle que représentée par le ministre de la Défense nationale,
2015
The project, entitled Situational Information for Enabling Development of Northern Awareness
(SEDNA), examined information sources of relevance to the Canadian north, and the exploitation
of these sources to support Maritime Domain Awareness (MDA). The project explored data
collection in the Canadian north and ex-filtration methods to move the information to a shore
facility. The ashore storage of the information was also considered through the development of a
geospatial system built from open-source technologies. The system now contains global
MDA-related information that supports numerous research activities as well as advice and data
delivery to Canadian Armed Forces (CAF) clients. The building of partnerships also represented a
significant component of the project. Collaboration with United States researchers pushed
forward the concept of semantic mediation among diverse information vocabularies, while
Canadian MDA partners both internal and external to DND were formed with the goal of better
exploitation of partner information sources relevant to the north. Finally, the combining of
northern information sources was explored to leverage the synergies of northern sources. These
project investigations have provided the CAF with immediate and direct delivery of advice and
data for current CAF activities, and with longer term research that will empower future delivery
of advice to clients.
The project Situational Information for Enabling Development of Northern Awareness (SEDNA)
focused on improving our understanding of maritime information sources relevant to the
Canadian north. The effort examined the geospatial storage and management of maritime data,
the use of data sets such as AIS and LRIT, the modelling of vessel tracks in the presence of ice
and complex coastlines, and joining of multiple northern systems via the cueing of one system
based on data available in a second system.
ii DRDC-RDDC-2015-R085
Le projet intitulé Situational Information for Enabling Development of Northern Awareness
(SEDNA) [information situationnelle pour permettre le développement d’une conscience
nordique] a servi à examiner les sources d’information pertinentes pour le Nord canadien et
l’exploitation de ces sources à l’appui de la connaissance du domaine maritime (Maritime
Domaine Awareness [MDA]). Tout au long de ce projet, on a étudié la collecte des données dans
le Nord canadien et les méthodes d’exfiltration pour transférer l’information à une installation
côtière. On a également tenu compte du stockage de l’information à terre en raison de la mise au
point d’un système géospatial à partir de technologies à source ouverte. Le système comprend
maintenant des renseignements mondiaux relatifs à la MDA pour soutenir diverses activités de
recherche ainsi que la prestation de conseils et la fourniture de données à l’intention des clients
des Forces armées canadiennes (FAC). Le renforcement des partenariats a aussi représenté une
composante importante du projet. La collaboration avec des chercheurs des États-Unis a aussi mis
à l’avant-plan le concept de médiation sémantique entre les divers vocabulaires de l’information,
tandis que les partenaires canadiens de la MDA, internes et externes au MDN, ont été formés
dans le but de mieux exploiter les sources d’information des partenaires qui sont pertinentes pour
le Nord. Enfin, on a examiné l’agencement des sources nordiques pour tirer parti de leur synergie.
Des enquêtes sur ce projet ont assuré aux FAC la fourniture immédiate et directe de conseils et de
données pour leurs activités en cours, tandis que la recherche à long terme permettra la prestation
future de conseils aux clients.
Importance pour la défense et la sécurité
Le projet Situational Information for Enabling Development of Northern Awareness (SEDNA) a
mis l’accent sur l’amélioration de notre compréhension des sources d’information maritime
pertinentes pour le Nord canadien. La démarche a permis d’examiner le stockage géospatial et la
gestion des données maritimes, l’utilisation d’ensembles de données (SIA et LRIT), la
modélisation de la trajectoire des navires en présence de glace et de littoraux complexes, et la
combinaison de multiples systèmes nordiques, par le déclenchement d’un système en fonction des
données qu’on retrouve dans un second système.
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Résumé …….. ................................................................................................................................. ii
Table of contents ............................................................................................................................ iii
List of figures .................................................................................................................................. v
List of tables .................................................................................................................................. vii
1.1.1 Work plan ............................................................................................................ 1
1.1.2 Milestones ........................................................................................................... 2
1.1.3 Outputs ................................................................................................................ 2
1.2 Outline ............................................................................................................................. 3
2 WBE 06EO01: Investigations into the uniqueness of the Arctic .............................................. 4
2.1 06EO01 activities ............................................................................................................ 4
2.1.1.1 Deployable MSA data collection and ex-Filtration .............................. 4
2.1.1.2 Multi-level data exploitation: Trial FETCH ......................................... 5
2.1.2 Validating vessel self-reporting .......................................................................... 5
2.1.3 VHF propagation in the north ............................................................................. 6
2.2 06EO01 summary and associated reports ........................................................................ 7
3 WBE 06EO02: Investigations of trusted information content and structure requirements
for MSA in the Arctic ............................................................................................................... 8
3.1 06EO02 activities ............................................................................................................ 8
3.1.1.1 Trust ..................................................................................................... 8
3.1.1.2 Semantics ............................................................................................. 9
Infrastructure (MSARI) ...................................................................... 10
3.1.2.2 Combining geospatial specifications .................................................. 11
3.1.2.3 Advice on the redesign of the Global Position Warehouse ................ 12
3.1.2.4 Advice on military use of AIS by HMC ships ................................... 13
3.2 06EO02 summary and associated reports ...................................................................... 13
4 WBE 06EO03: Enhancing MSA information exchange and utilization with partners ........... 14
4.1 06EO03 activities .......................................................................................................... 14
5 WBE 06EO04: Investigate information exploitation that contributes to Arctic MSA ............ 18
5.1 06EO04 activities .......................................................................................................... 18
5.1.2 High Fidelity Northern Simulator ..................................................................... 19
5.1.3 Data mining ....................................................................................................... 19
5.1.5 Global vessel identification ............................................................................... 21
5.1.6 Vessel path interpolation in the presence of coastline and ice .......................... 22
5.2 06EO04 summary and associated reports ...................................................................... 23
6 Concluding remarks: Assessment & Way Forward ................................................................ 24
6.1 06EO assessment against proposal ................................................................................ 24
6.1.1 Objective ........................................................................................................... 24
6.1.3 Technical Merit ................................................................................................. 25
6.1.4 Proposal assessment .......................................................................................... 25
6.3 Final evaluation ............................................................................................................. 26
6.4 Lessons learned ............................................................................................................. 26
6.5 Looking forward ............................................................................................................ 27
List of figures
Figure 1 The SEDNA VDR data flow, Iridium backhaul route (in black), and the
AIS backhaul route (in red). Reproduced from [2]. . . . . . . . . . . . . . . . . . . . . . 5
Figure 2 Temporal alignment of sensed (i.e., radar) and broadcast (i.e., AIS)
vessel positions. The radar data are represented by the contours of
probability while the AIS data represent broadcasts from two different
objects. Reproduced from [6]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Figure 3 The relationships between trust components (left column) and data
modelling activities/components (right column). Reproduced from [10]. . . . . . . . . . . . 9
Figure 4 The inferred vocabulary relationships are represented by green lines.
User defined relationships are indicated by black lines. Based on a
figure in [14]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Figure 5 The Maritime Situational Awareness Research Infrastructure (MSARI)
is composed of numerous external maritime data feeds (left side), a
collection of parsing, processing, storage, and application development
computers (middle), a data model particular to the maritime data being
assembled, and end users (right side). Reproduced from [17]. . . . . . . . . . . . . . . . . 11
Figure 6 The design model that schematically shows the combining of Arc
Marine and ISO 19115 to form a data model used for acoustic data
management. Reproduced from [20]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Figure 7 The GeoNetwork interface. A geospatial-temporal search capability
allows the identification of products. A product summary (see lower
half of the figure) briefly described the product and displays a small
image of the product. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Figure 8 The MSSIS system aggregates data from contributing national, coastal
AIS receivers. The TV32 software (shown here) is used to visualize the
receptions. JTFN J2 was interested in the potential of MSSIS as a
distribution mechanism for the Canadian northern picture. . . . . . . . . . . . . . . . . . 16
Figure 9 An example of LRIT positional reports for a foreign vessel that transited
into Canada’s 1000 nautical mile limit near Alaska and docked near
Delta, British Columbia. Pink balloons represent LRIT positional
reports. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Figure 10 The northern simulator interface and displayed results. A vessel track
(green and red line) is shown with an aircraft track (red line). The
simulation involved a single vessel transiting from the Labrador Sea,
through Hudson Strait, into Foxe Basin. . . . . . . . . . . . . . . . . . . . . . . . . . 19
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Figure 11 The SEDNA cueing algorithm generated entry and exit alerts for the
vessel track indicated as a yellow line crossing the red circular line. The
zoomed in portion of the figure confirms the vessel crossed the red line
boundary twice. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Figure 12 Alterations to the vessel track in the presence of ice. The ice field is
shown in blue, with open water surrounding the ice. Interpolated vessel
tracks are shown as grey or black lines. The left panel shows higher or
more restrictive ice conditions as compared to the left. Thus in the left
panel, some vessel paths go around the ice field where as in the right
panel all paths traverse the ice field. Reproduced from [54]. . . . . . . . . . . . . . . . . . 23
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Table 2 The Milestones of 06EO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
viii DRDC-RDDC-2015-R085
Acknowledgements
Numerous people contributed to the results of SEDNA, as indicated by the authorship of the
reports and papers contained within this report. Outside of the author contributions, we
acknowledge the support and advice provided by Dr. Mark McIntyre. Mark’s critical questioning
of direction and results provided the project team with new insight into how information should
or could be used to advance our understanding of maritime traffic in the Canadian north, and for
this we are grateful. Also to be acknowledged are personnel at Chief of Defence Intelligence,
Helicopter Operational Test and Evaluation Facility, and Maritime Atlantic N6 who recognized
value in the SEDNA project and as a result engaged us directly to help support their activities.
DRDC-RDDC-2015-R085 1
1 Introduction
In recent years there has been increased dialogue and focus on the Canadian north. Much of this
focus is on the environment, and how changes in climate and sea ice conditions impact the local
human, animal, and plant populations. However, such changes also impact patterns of movement.
One obvious example is how changing ice conditions impact a vessel’s ability to transit through
Canadian northern waters.
The Government of Canada has a stated interest in the Canadian north [1] and numerous
government departments have mandates that intersect with the north. In terms of the Department
of National Defence (DND), the intersection occurs in the area of defending the national border.
Part of this defence involves developing and maintaining an awareness of activity in the north.
As a means of progressing DND solutions related to northern awareness, DRDC initiated a
northern-focused Applied Research Project (ARP). The project Situational Information for
Enabling Development of Northern Awareness (SEDNA), placed attention on research into the
exploitation of information pertaining to the maritime domain in the Canadian north. The aim of
the project was an improved understanding of how existing and new maritime data sources and
technologies influence our generation of awareness within the northern maritime domain.
The following report documents the SEDNA project. As such, the report integrates the research
effort over the life of the project, providing a holistic context to what would otherwise be the
individual efforts that made up the project.
1.1 The project - 11HO, 11JO, 06EO
The SEDNA project was initially funded under the 1H thrust Maritime Domain Awareness. The
ARP began in April 2011 under the DRDC project code 11HO. With project code changes in
2012, the project became 11JO. DRDC Program Formulation resulted in another code change and
in 2013 the project became 06EO.
The project was initially scheduled to last four years. However, funding reallocations that resulted
from DRDC Program Formulation resulted in the early termination of the ARP in March 2014.
The work conducted under 06EO was the responsibility of the Maritime Information Support
(MIS) group (i.e., formerly the Maritime Information and Knowledge Management (MIKM)
group) at DRDC – Atlantic Research Centre (formally DRDC Atlantic). MIS, which now exists
in the Maritime Decision Support (MDS) section, was responsible for the organization, execution,
and reporting associated with the project.
1.1.1 Work plan
The Work Breakdown elements (WBE) for 06EO, defined in the project proposal, are shown in
Table 1. The sections that follow describe the work conducted as part of these WBEs.
2 DRDC-RDDC-2015-R085
WBE No. Description
WBE 06eo03 Enhancing MSA information exchange and utilization with partners
WBE 06eo04 Investigate information exploitation that contributes to Arctic MSA
1.1.2 Milestones
The milestones as defined in the project proposal are shown in Table 2.
Table 2: The Milestones of 06EO.
Date Milestone Description
May 2012 Identify implications of unique Arctic situation on data and research
algorithms
(OGDs)/partners for potential information utilization
October 2013 Modify/develop research algorithms for unique Arctic conditions
June 2014 Provide feedback/output to DND and partners
March 2015 Final reporting
Predominate outputs from the project can be summarized as including:
reports in the form of DRDC Technical Memoranda and letter reports,
conference and journal papers,
presentations on any of the above.
All project output is noted in the Work Breakdown element sections that follow.
1.2 Outline
This report provides a review of work conducted as part of the SEDNA ARP. More importantly,
this report provides the holistic context, integration, and interpretation of 06EO activities.
Section 2 describes WBE01 and includes work that examined leveraging information for multiple
purposes, and the movement of data and information from the north. In the area of information
leveraging, a trial conducted in 2012 allowed the opportunity to collect local information of
relevance to the maritime traffic, and explore various transfer mechanisms for getting those data
back to appropriate centres.
WBE02 (Section 3) examines trusted information content and structures that might be used to
transfer and manage the information once received. This section includes an investigation of trust
as framed in the context of a data model structure and a second study on the management of
information semantics. As well, the section examines geospatial data structures pertinent to
maritime domain awareness (MDA).
WBE03 (Section 4) focused on building the partnerships that are critical for work in a multi-
jurisdictional environment such as the Canadian north. The outreach activities that spanned
departmental, inter-departmental, and international groups are described. This outreach produced
positive relationships that improved the impact and deliverables of this project.
WBE04 (Section 5) then examines the exploitation of data and information. Data sources
obtained via relationships built in the previous WBE are leveraged to explore the relevance of the
data sources to the north. As well, techniques for using the information are explored for both
discovery and system-to-system leveraging.
Section 6 provides concluding remarks. In this section the results of 06EO are linked to the
broader Science & Technology (S&T) plans of DRDC.
2 WBE 06EO01: Investigations into the uniqueness of the Arctic
Work under this breakdown element was focused on forming an understanding of the
implications of the Arctic situation on MDA data and research algorithms. As a result, the
research effort focused on understanding how the environment and the location of the Arctic
(i.e., the polar region of the earth) impacted the maritime data and acquisition of those data.
2.1 06EO01 activities
2.1.1 CFAV QUEST trial Q346
The SEDNA project leveraged CFAV QUEST during its 2012 voyage in support of the Northern
Watch (NW) Technology Demonstration Project (TDP). This leveraging was focused on two
primary areas:
an investigation into the collection and exfiltration of MDA data through use of
commercial-off-the-shelf (COTS) equipment
an investigation of tactical information that may be of operational use
2.1.1.1 Deployable MSA data collection and ex-Filtration
The goal of this effort was to design and test methods for collection and ex-filtration of MDA
data [2] from the Canadian north with QUEST trial Q346 providing an opportunistic platform for
experimentation. A commercial Voyage Data Recorder (VDR) provided the onboard storage
mechanism for the QUEST’s local recognized maritime picture (RMP). The constructed onboard
system, depicted in Figure 1, collected local Automatic Identification System (AIS) messages,
local radar contacts, data from the Automatic Dependent Surveillance-Broadcast (ADS-B) system
on commercial aircraft, and the QUEST’s own-position data. The system combined these
disparate data sources into a single package and transferred the data back to DRDC Atlantic.
The transfer mechanism investigated as part of this effort included the use Military Iridium
9522B L-band Transceiver using a military SIM card. The backhaul took place through the
satellite gateway in Hawaii. According to Canadian Armed Forces (CAF) sources responsible for
distribution and use of the SIM cards [3], this was the first time this specific backhaul mechanism
was successfully used.
An alternate backhaul experiment used the commercial exactEarth (eE) satellite system. AIS
message type 8 in the AIS specification allows a user defined binary message to be incorporated
into an AIS type 8 message. This means a user can construct a specific binary message, and
encapsulate this inside the type 8 AIS message. A total of four different SEDNA-specific
messages were created and packaged inside AIS type 8 messages. These messages were then
transmitted from the transponder on QUEST, and subsequently received by the eE satellites as
part of their normal received data. Effectively, we were utilizing the eE satellites as a carrier to
ultimately bring the data back to DRDC Atlantic.
Figure 1: The SEDNA VDR data flow, Iridium backhaul route (in black), and the AIS backhaul
route (in red). Reproduced from [2].
The SEDNA data set collected as part of Q346 was subsequently processed [4] and inserted into
the MIS research infrastructure (see Section 3.1.2.1) for use by the group.
2.1.1.2 Multi-level data exploitation: Trial FETCH
The availability and use of tactical information in an operational setting was explored as part of
the Trial FETCH. This trial was a subcomponent of Q346. FETCH has been reported on
separately to numerous clients [5] and is mentioned here for completeness.
2.1.2 Validating vessel self-reporting
The Arctic is often considered remote, in the sense of distance from population areas and
infrastructure. These remote characteristic elevate the importance of remote sensing assets, since
local assets are minimal and often expensive to establish. However, the ability to remotely
“sense” an object is vastly different from modern data sources that broadcast a vessel’s position,
such as AIS.
Research into the validation of AIS positional reports was conducted under SEDNA [6]. This
research involved the use of Kalman smoothing to both forward and hindcast a vessel’s radar
contact position to a moment in time that aligns with available space-based AIS data. The
temporally co-located data may then be compared and statistically tested to determine the
probability of the AIS and radar data referring to the same object.
One such comparison using simulated data is shown in Figure 2. The figure indicates two
co-temporal AIS receptions with a temporally shifted radar contact. The contours refer to the
estimated vessel location based on radar. Thus, one can consider the contours as indicating the
probability of the sensed vessel being located within the ovals formed by the contours. The figure
indicates the sensed object is more likely responsible for AIS message 1 (i.e., denoted AIS 1) as
compared to AIS message 2.
Figure 2: Temporal alignment of sensed (i.e., radar) and broadcast (i.e., AIS) vessel positions.
The radar data are represented by the contours of probability while the AIS data represent
broadcasts from two different objects. Reproduced from [6].
2.1.3 VHF propagation in the north
AIS data are well established as a key information component for the maritime picture. Since AIS
messaging takes place through the Earth’s atmosphere, SEDNA conducted a literature review [7]
to identify important environmental factors related to AIS propagation (note, AIS transmissions
are in the very high frequency (VHF) band).
The resulting report reviewed tropospheric and ionospheric influences, land and sea influences,
and atmospheric influences. The existence of the International Telecommunications Union (ITU)
model for VHF propagation was noted. The model exists in the form of a spreadsheet, and models
positional characteristics of multiple antennas to determine power levels over sea or land. Model
calculations indicated that surface propagation ranges of 120 km could be expected to occur in
some cases. Ducting was also noted to be an important factor in extending reception range.
Ducting often results from variations in the refractivity of air. This investigation provided general
information that was useful to the team throughout the SEDNA project.
2.2 06EO01 summary and associated reports
The formation of MDA in a remote area void of infrastructure will rely on data collection and
ex-filtration techniques that fully utilize the available assets. Government of Canada vessels that
stream their local maritime picture back to operations centres through varied communication
pathways is one example of such asset utilization. The combining of data sources for validating
the information content will also be an important factor in an area with minimal data resources.
This WBE explored these facets through reports and presentations including:
Field trials specific to the north: [2], [4], [5]
Association of sensed and broadcast vessel data: [6]
Environmental influence on AIS propagation: [7]
3 WBE 06EO02: Investigations of trusted information content and structure requirements for MSA in the Arctic
Work conducted under WBE02 can be divided into two categories. The first category deals with
the information content and covers the topics of trust and semantics. The second category deals
with the structures used to store the information.
3.1.1 Trust and semantics of information
3.1.1.1 Trust
The issue of trust in information was a focal topic of 11HL Technologies for Trusted Maritime
Situational Awareness [8], a predecessor ARP to SEDNA. SEDNA built upon that previous effort
by identifying the components of trust [9], examining how trust relates to data models [10], and
how consistency in available information is related to trust [11, 12].
The components of trust were first identified by a literature review on the topic of trust between
humans [9]. These trust components were then related to the parts of an information system, those
parts being source, data, delivery, and processing. The investigation indicated that source,
delivery and processing were most dependent upon the trust components, indicating that to
improve trust in the system, one could concentrate the effort on improving how the source,
delivery mechanism, and processing components may be modified to demonstrate the trust
components to the user.
The work also examined how AIS timing information may influence user trust. By volume,
AIS data are the largest single contributor to the RMP and thus plays an important role in the
awareness of on-water objects for the Royal Canadian Navy (RCN). However, timing information
available in AIS broadcasts only include the second’s value (i.e., no minutes or hours are
indicated) associated with the transmission time. Errors in the second’s value can have serious
implications on the organisation and use of AIS information.
The investigation indicated that timing inconsistencies in the AIS data negatively impact the
user’s trust in the source. It was speculated that this indicated recognition that the source is
ultimately the responsible party for the data it generates.
A related activity under this WBE was an examination of trust in data models [10]. The trust
components identified in [9], were used to assess the parts of an information system, and in
particular the tasks involved in data modelling. Figure 3 illustrates one of the findings, namely the
components of trust (left column) that were identified to influence the data modelling activity
(right column).
Many of the relationships are intuitive; for example, documentation influencing understandability
and usefulness of the data model. Perhaps more innocuous is the relationship between reliability
and metadata. The relationship indicates that metadata, once they are incorporated into the data
model, provide a means for the user to assess the quality of the data made available by the data
model. Thus, during the database design activity, one should include information that is useful for
assessing data credibility.
Figure 3: The relationships between trust components (left column) and data modelling
activities/components (right column). Reproduced from [10].
3.1.1.2 Semantics
Information is typically distributed based on a known or implied vocabulary. For example, the
transmission of positional information in an AIS message implies that the particular field
containing the value 45.1234 is latitude in degrees north. The term latitude is part of a larger
vocabulary that the AIS community-of-interest understands and has agreed to as part of their
common lexicon.
Working with United States (US) university colleagues under the Marine Metadata
Interoperability Project (funded by the US National Science Foundation), a group of information
specialists described and defined pertinent metadata attributes for the use of the global
community [13]. As well, the use of semantic technologies for mediation of vocabularies was
explored [14].
Figure 4 illustrates one of the important concepts of semantic mediation technologies.
Vocabularies exist for specific communities of interest and building relationships between these
community vocabularies is typically a manual and time consuming process. However, advances
in semantic mediation software means that some relationships can be automatically inferred and
generated. Figure 4 shows four vocabularies (i.e., Voc1, Voc2, etc.). User determined
relationships are illustrated as black lines. Automatically inferred relations are shown as green
lines. The figure shows that three user defined mappings are required to join all four vocabularies.
Figure 4: The inferred vocabulary relationships are represented by green lines. User defined
relationships are indicated by black lines. Based on a figure in [14].
3.1.2 Information infrastructures
The infrastructure used for the management of information is largely dependent on the type and
anticipated usage of the information. The SEDNA project provided an opportunity for research
into new methods of information management, as well as new methods of combining existing
specifications for information storage. This type of research in turn lead us to providing advice to
the redesign of existing RCN ashore systems such as the Global Position Warehouse (GPW)
(later to be renamed the Naval Positional Repository; NPR).
3.1.2.1 The Maritime Situational Awareness Research Infrastructure (MSARI)
Under 11HL Technologies for Trusted Maritime Situational Awareness [8], there was a
recognition of the importance of global data aggregation systems and the groups that contribute to
these systems. For example, groups such as the MDA Data Sharing Community of Interest and
systems such the Marine Safety and Security Information System (MSSIS) [15] provide a wealth
of information in the form of partnerships, but also in the form of data aggregation.
To explore issues related to maritime data aggregation and large volume data management
and processing, SEDNA developed the Maritime Situational Awareness Research
Infrastructure (MSARI) (Figure 5). The system now forms the backbone of much of the
MIS group’s research involving maritime data.
The MSARI development began with a detailed design specification that outlined both the intent
and functional requirements of the infrastructure [16]. The infrastructure was assembled with the
open source PostgreSQL database management system (DBMS) and PostGIS extensions to create
a fully geospatial DBMS. MSARI now automatically ingests maritime data from three sources:
MSSIS; eE, and a DRDC receiver in Halifax Harbour. Data volumes are approximately
1.4 billion positional reports per month, which equates to about 1 terabyte of data per month.
Figure 5: The Maritime Situational Awareness Research Infrastructure (MSARI) is composed of
numerous external maritime data feeds (left side), a collection of parsing, processing, storage,
and application development computers (middle), a data model particular to the maritime data
being assembled, and end users (right side). Reproduced from [17].
3.1.2.2 Combining geospatial specifications
Numerous specifications and standards exist for the management of data and information.
One effort conducted under SEDNA, in collaboration with Underwater Sensing Section
(DRDC – Atlantic Research Centre) and with Department of Fisheries and Oceans, investigated
combining two such specifications: the Arc Marine data model [18] and the International
Organization for Standardization (ISO) 19115 Geographic Information – Metadata standard [19].
Arc Marine is a framework for geospatial oceanographic databases. The framework is built on the
ESRI model (thus the use of the “Arc” prefix). Although some have suggested that Arc Marine is
built specifically for ESRI geospatial products, it is actually a set of oceanographic information
structures that may be applied in a wide variety of application environments. In the work
conducted as part of SEDNA, the PostgreSQL and PostGIS environments (i.e., the same
environments as used for MSARI) were used to host the Arc Marine model [20] (Figure 6).
Figure 6: The design model that schematically shows the combining of Arc Marine and
ISO 19115 to form a data model used for acoustic data management. Reproduced from [20].
3.1.2.3 Advice on the redesign of the Global Position Warehouse
The knowledge gained as part of the MSARI development positioned the MIS group working on
SEDNA as experts regarding both AIS and infrastructures for maritime data. As a result, the
MIS group was requested to provide database design advice to Maritime Atlantic
(MARLANT) N6 [21]. At the time, MARLANT N6 was redesigning their data processing and
management system for the global maritime data that currently flow into MARLANT.
The redesign comments provided to N6 indicated numerous points that should be considered as
part of the redesign. One point was particularly relevant for advancing the understanding of the
new system. The redesign effectively produced a reversal of the database placement and the
Global Command and Control System-Maritime (GCCS-M) processing. In the previous system,
GCCS-M preceded the database in the flow of data. In the new system, the database precedes
GCCS-M. This means the maritime data flowing into the Regional Joint Operations
Centre (RJOC) is not limited by GCCS-M processing capabilities. As a result, the full maritime
data stream is now available to the operators via the database, effectively providing them with a
factor of 10 increase in data availability.
MIS involvement in the N6 redesign effort continued over the period of SEDNA. The
contribution of MIS during SEDNA lead to a Directed Client Support [22] request from N6 to
DRDC in 2013.
3.1.2.4 Advice on military use of AIS by HMC ships
The SEDNA team’s experience in understanding and managing AIS information
(see Sections 2.1.1.1, 3.1.2.1 and 3.1.2.3) provided the foundation knowledge for MIS members
to address a request for comment [23] on a draft RCN Standard Operating Procedure (SOP) and
Tactical Notice (TACNOTE). The SOP and TACNOTE addressed the use of AIS by HMC ships
in both receive and transmit mode. The provided feedback emphasised the need to clarify AIS as
a broadcast system as opposed to a sensor, and also the need to clarify the AIS operation states for
the HMC ship or associated helicopter. For example, in most cases the ship or helicopter
AIS transponder will be in receive mode only; while in other cases (e.g., search and rescue) the
ship/helicopter may have a requirement to transmit AIS messages to inform others involved as to
their location.
Information regarding maritime traffic in the Canadian north will likely be sparse, and originate
from varied communities (e.g., different government departments). As a result, the trust placed on
the information may be varied based in part on the originator. Understanding how trust may be
influenced by available metadata helps shape future system development to ensure the proper
incorporation of metadata standards into the new systems. As well, the communities will likely be
using varied vocabularies and understanding vocabulary management techniques has the potential
to help the RCN in both domestic and international activities. The SEDNA leveraging of open-
source geospatial technologies for maritime data management has also proven to be timely, with
this allowing direct advice to the RCN on redesign efforts currently taking place at ashore centres.
The reports supporting this WBE include:
trust and semantics of information used to help form situational awareness: [9, 10], [12], [14]; and
information infrastructures for developing situational awareness: [16], [20], [21], [23].
4 WBE 06EO03: Enhancing MSA information exchange and utilization with partners
Work conducted under WBE03 was designed to build technical and research relationships with
other government departments (OGDs) and other organizations such as NORAD. The existence
of this WBE effectively recognises that Canadian MDA requires collaborations among many
partners. Developing those relationships and maintaining the awareness among community
members was the emphasis of this WBE. Specifically, the outreach targeted DND, OGDs,
NORAD, and international defence research.
4.1.1 DND outreach
The outreach to DND focused on the potential to deliver prototype research products to DND and
CAF clients. In 2011 we leveraged DRDC Agility Funds to investigate geospatial product
management software called GeoNetwork. This work was initiated based on a requirement from
MARLANT N6 and CF intelligence officers for geospatial product management. The work
complemented the SEDNA project by providing a mechanism for delivery of experimental
products from DRDC Atlantic to MARLANT.
The GeoNetwork investigation [24] showed promise for the DND environment. An instance of
GeoNetwork was established at DRDC Atlantic (Figure 7). The capabilities of GeoNetwork were
socialized via numerous briefings provided to the MARLANT GeoTech community, RJOC(E),
MSOC, and MSOC project staff [25-27]. The automatic creation of the AIS index [28] also
provided the project with a test geospatial product to be loaded into the GeoNetwork instance at
DRDC Atlantic and this ability was demonstrated.
During the project, national interest in GeoNetwork also emerged. In 2014 ADM(IM) was
considering GeoNetwork for national DND geospatial product management. We have provided
our expertise as required to ADM(IM) including the results of our specific investigation.
Outreach to DND also included an effort to engage the air community. Discussions with the
Helicopter Operational Test and Evaluation Facility (HOTEF) at Shearwater NS provided an
opportunity to assist the Maritime Helicopter Operations Support Centre in the preparation and
training of maritime helicopter crews [29]. For this effort, realistic maritime traffic situations
were provided in the form of AIS contact data for four areas of the globe. This effort leveraged
the MSARI development noted in Section 3.1.2.1. This also provided MIS personnel with an
opportunity to tour the HOTEF facility and identify how the supplied data were being used in the
maritime traffic simulator.
Figure 7: The GeoNetwork interface. A geospatial-temporal search capability allows the
identification of products. A product summary (see lower half of the figure) briefly described the
product and displays a small image of the product.
Outreach to Joint Task Force North (JTFN) was also initiated during OP NANOOK 2013
[30, 31]. Personnel from DRDC Atlantic joined JTFN J2 during the Resolution Island portion of
OP NANOOK 2013. The uniqueness of the arctic situation with respect to challenges introduced
by adventurers and explorers was evident in J2’s preparation for both the Operation and daily
activities.
During the visit, we also provided JTFN J2 with advice on the use of MSSIS (Figure 8). At the
time, MSSIS was being suggested as a possible distribution mechanism for building Arctic
domain awareness among international partners. The MIS group has considerable knowledge on
MSSIS, as we were the first Canadian contributor to the system in 2008. MIS staff recommended
to JTFN J2 that MSSIS not be used for distribution of the Arctic maritime picture. This was in
part due to the varied distribution limitations on the data that makes up the Arctic picture, and
also due to the controlled but nevertheless freely shared nature of the data on the MSSIS. JTFN J2
later briefed CO JTFN using MIS advice and it was subsequently decided that MSSIS would not
be used for distribution of the Canadian Arctic picture. The JTFN outreach continues to guide
some of the specific activities that are ongoing as part of the DRDC Maritime Information
Warfare (MIW) program area and specifically the use of classified information sources.
Figure 8: The MSSIS system aggregates data from contributing national, coastal AIS receivers.
The TV32 software (shown here) is used to visualize the receptions. JTFN J2 was interested in the
potential of MSSIS as a distribution mechanism for the Canadian northern picture.
4.1.2 OGD outreach
Outreach to other government departments (OGDs) was primarily through the Canadian
Interdepartmental Marine Security Working Group (IMSWG). However, direct interaction with
Fisheries and Oceans Canadian Coast Guard (CCG) was also part of the outreach. In a similar
manner, presentation to a Canadian arctic security conference helped inform the community of
the SEDNA research.
The IMSWG was briefed [32] on the development and application of the High Fidelity Northern
Simulator (see Section 5.1.2). The simulator was used to model surveillance options for vessels
moving to/from the Mary River iron ore project that was at the time, being proposed on Baffin
Island. This presentation was at the request of Transport Canada, who was previously briefed on
the capabilities of the northern simulator [33].
Results from the Northern Simulator were also used to brief the larger Canadian security
community via the Maritime & Arctic Security & Safety (MAS) Conference [34]. MAS is an
annual event that brings together Canadian private and public sector staff with an interest in
safety and security in the Canadian north. The simulator was used as a basis for assessment of
space-based AIS as the primary surveillance mechanism for the Canadian north.
Relationships were also formed with Fisheries and Oceans CCG, and in particular the
CCG Marine Policy group. CCG represents the largest single contributor of Canadian maritime
data to the national system. Negotiations with CCG resulted in SEDNA acquiring one full month
of raw Long Range Identification and Tracking (LRIT) data for the Canadian AOR. A data use
agreement was established and MIS group then used the LRIT data in a follow-on study
(see Section 5.1.1).
4.1.3 International outreach
Opportunistic briefs were provided to the Senior Canadian Liaison Officer
NORAD-NORTHCOM Science & Technology Section [35, 36] as a means to build an
understanding of SEDNA and DRDC capabilities. The first of these briefs provided a full
description of SEDNA and this was followed later in the same year with an update. The update
was provided as part of the larger DRDC effort in the Canadian north. Although opportunities
were explored for further collaboration with NORAD, human resources greatly limited our ability
to directly engage NORAD staff.
Other international outreach focused on The Technical Cooperation Panel (TTCP) Maritime
Group Technical Panel–1 (TTCP MAR TP-1) [37]. This presentation focused on the need for
information management and how this requires proper information structures. The information
science aspects of the problem must be accounted for including issues introduced by data
volumes, acquisition diversity, user access, and anticipated usage. This presentation also
introduced the concept of value of information and big data for MDA exploitation, two topics that
are now present in the MIW programme.
MDA for Canada’s north is a whole-of-government endeavor. Numerous departments contribute
to all aspects of MDA development, including such things as the development of regulations that
help define the maritime data to be provided by the vessel community. At the moment,
understanding the existing Canadian information flow that supports MDA requires the
establishment of personal relationships among the stakeholders. From an information perspective,
what appears to be missing from the present day system is an overarching technical coordination
among the various stakeholders. Such coordination is required to construct a coherent and
well-understood Canadian MDA information system. Reports and presentations in this WBE
included:
OGDs: Reports and presentations include [32-34]; and
International: Outreach to NORAD [35, 36] and TTCP MAR TP-1 [37].
5 WBE 06EO04: Investigate information exploitation that contributes to Arctic MSA
Work conducted under WBE04 was designed to exploit data and information obtained through
the partnerships built under WBE03, leveraging those relationships and data sources for improved
CAF arctic maritime domain awareness.
5.1.1 Leveraging data sources: AIS and LRIT
As noted in Section 4.1.2, SEDNA relationships with DFO resulted in CCG providing raw
LRIT data to the SEDNA team; specifically data from the Canadian AOR for October 2010.
An analysis of the LRIT data (example shown in Figure 9) was conducted with supporting
AIS data [40]. The analysis presented a detailed examination of LRIT timing, vessel identification
in LRIT, and frequency of reporting. The analysis also considered how combining the AIS and
LRIT information can improve maritime situational awareness.
Figure 9: An example of LRIT positional reports for a foreign vessel that transited into Canada’s
1000 nautical mile limit near Alaska and docked near Delta, British Columbia. Pink balloons
represent LRIT positional reports.
The LRIT system in October 2010 was in the initial operating phase and as a consequence results
of the investigation may be biased due to issues related to system start-up. Nevertheless, the
results of the investigation showed that LRIT and AIS provide complementary information for the
formation of maritime domain awareness with minimal redundancy.
One important feature of LRIT as compared to AIS is the ability to poll onboard LRIT equipment.
Such polling has direct applicability and usefulness in the Canadian north. For example, one
could envisage the polling of vessels entering a specific northern area, thus providing the
maintenance of vessel track characteristics to a prescribed level of detail appropriate for the area.
5.1.2 High Fidelity Northern Simulator
Obtaining MDA in the Canadian north will require an assortment of monitoring assets. As such,
determining the combined capability offered by a diverse set of assets was considered applicable
to SEDNA. To assess the capability offered, SEDNA leveraged the Systems Tool Kit (STK 1 ),
developed by AGI [41], to develop a high fidelity northern simulator.
The simulator [42] is a numerical model that provides a mechanism to assess monitoring options.
The simulator models the placement of assets (e.g., aircraft with sensors, ground stations) to be
combined with modelled ship traffic. Moving the ship traffic through the modelled area then
allows the model to determine when specific monitoring assets may be in contact with the
ship traffic.
Simulator results for the Mary’s River scenario is shown in Figure 10. This simulation included
the eE satellites in orbit as of 2013, a modelled AIS ground receiver at Foxe Peninsula on
Baffin Island, a modelled aircraft with radar and AIS capability, and a modelled vessel that
transits into and out of Foxe Basin.
Figure 10: The northern simulator interface and displayed results. A vessel track (green and red
line) is shown with an aircraft track (red line). The simulation involved a single vessel transiting
from the Labrador Sea, through Hudson Strait, into Foxe Basin.
5.1.3 Data mining
As data volumes grow, techniques that allow automated information generation become more
important. Since AIS data are a major source of maritime data, a data mining investigation
involving AIS data was initiated to determine if mining techniques were of sufficient maturity to
1 STK was previously known as the Satellite Tool Kit.
be used by operators. This work was developed in partnership with RCN intelligence officers at
MARLANT.
The data mining investigation leveraged Agility funding and MSARI (see Section 3.1.2.1). Data
mining, characterized as finding previously unknown or hidden information in data sets [43], was
applied to AIS data aggregated in MSARI from the MSSIS and eE sources. The data set covered
approximately a one week period, and totalled 4.3 million vessel reports.
The investigation [44] reviewed the maturity of data mining technologies. The results indicated
that those data mining technologies with higher-level graphical user interface (GUI) design, still
require considerable user knowledge to properly plan and conduct a data mining activity.
Lower-level technologies require more direct code development, and require advanced or perhaps
specialized user knowledge to fully exploit the technology. The conclusion was that substantial
advancement in these technologies is required before they become useful to the operator
community as an investigative tool.
Nevertheless, the tools are useful in identifying previously unknown patterns in the data. Using
the lower-level technologies, an investigation identified relationships in the movement of vessels
between ports in the southern US [45]. Such a technique could be applied to the Canadian north.
However, data sparseness would likely be a limiting factor as this would reduce confidence in the
results.
SEDNA was one northern-focused activity within DRDC. A second major northern-focused
effort is the Northern Watch (NW) Technology Demonstration Project (TDP). There was no
formal relationship between SEDNA and NW; however, informal connections were developed
over the course of the projects.
SEDNA efforts were designed to enhance northern domain awareness and as such considered the
wide-area domain. Northern Watch was a project that investigated localized data collection to
support awareness of maritime traffic in the north. Often relationships between wide-area and
local efforts consider the localized sources as an information producer for the wide area picture.
However, the reverse can also provide benefit-the wide area data sources can support the local
sensing efforts.
SEDNA established MSARI as a backbone system to support the data requirements of the project.
Since MSARI contained northern AIS data as obtained from eE, these data could be used to
indicate expected vessel traffic in the area of the NW site. Indication of a vessel advancing
towards the sensor location may be useful in those instances when sensors require
preconditioning before use. An algorithm was developed and an alert mechanism demonstrated
for indicating the approaching vessels [17, 46]. The algorithm was demonstrated using historic
data from May 2013 (Figure 11).
Advanced warning for a local sensor suite could utilize other information sources beyond
space-based AIS. For example, space-based radar collection and processing could be used to
localize a contact given appropriate contact size, ice, and sea state conditions and therefore cue
the algorithm. Other such wide-area surveillance technologies such as airborne Light Detection
and Ranging (LIDAR) or high frequency surface wave radar (HFSWR) could also be used.
Figure 11: The SEDNA cueing algorithm generated entry and exit alerts for the vessel track
indicated as a yellow line crossing the red circular line. The zoomed in portion of the figure
confirms the vessel crossed the red line boundary twice.
5.1.5 Global vessel identification
Space-based AIS provides the ability to track broadcasting vessels around the world. Although
AIS provides an identity, the validity of this information and the fact that many vessels do not
broadcast AIS, means the identity of all vessels within this global set remains problematic.
Considerable work was conducted during SEDNA on the development of a statistically optimal
identification approach for the entire global vessel data set [47]. Although applied to the global
vessel set, the algorithm is also applicable to isolated subsets of vessels that have particular
operating characteristics; for example, the ability to operate in ice infested waters.
The algorithm uses characteristics of vessels to define the statistically optimal identity.
Effectively, characteristics of vessels, as determined from the sensed or broadcast data, are
matched against the characteristics as determined from authoritative sources, such as would exist
in vessel databases. As an example, data points that indicate a vessel is operating in ice imply that
this vessel has ice capability. Similarly, a vessel transiting the North Pacific implies certain
characteristics about available fuel capacity.
Computations involving global data sets are not trivial. Many computational challenges exist
involving both the complexity of the computations [48] and the data volumes [49]. This work is
ongoing with application to other and more diverse maritime data sets.
5.1.6 Vessel path interpolation in the presence of coastline and ice
Two characteristics that set the Canadian north apart from the Canadian east and west coasts, is
the complexity of the coastline and the presence of ice. The complex coastline is a result of the
94 major islands and 36,000 minor islands [50] that make up the archipelago. The ubiquitous
nature of the ice adds to the already difficult northern operating condition.
Vessel tracks in the presence of a complex coastline were the focal point of one SEDNA
investigation. An Interpolation System (IS) was built [51] to utilize the contact reports within
MSARI. The IS was designed to meet requirements that focused on answering geospatial
questions (Figure 12) such as which vessels were likely to pass through defined marine areas.
Dealing with the complex northern coastline also introduces complexities related to coastline
resolution and the determination as to whether or not the contact positions should be discarded
due to spatial errors [52]. This, combined with the volume of maritime data, results in a
computationally intensive process that introduces questions such as distributed processing and
database management.
Interpolation between vessel positional reports in the presence of ice was also investigated.
Previous DRDC research on probabilistic interpolation [53] was enhanced to include vessel track
interpolation through or around a simulated ice field [54]. Parameterization of a vessel’s ice
capability combined with modelled ice characteristics, effectively results in the introduction of a
temporal cost to the vessel’s transit through a modelled ice infested area. This results in the
temporally shortest vessel path being related to the characteristics of the vessel (e.g., vessel speed
through the ice), ice conditions (e.g., ice coverage) and the geography (e.g., the physical expanse
of the ice).
The interpolation technique shows how variations in these characteristics impact the vessel’s
shortest path through the ice field (Figure 12). In some situations, the ice field and vessel
characteristics result in the interpolated vessel path going around the ice, while in less restrictive
ice conditions (or using a vessel with greater ice capability) the vessel proceeds through the ice.
Figure 12: Alterations to the vessel track in the presence of ice. The ice field is shown in blue,
with open water surrounding the ice. Interpolated vessel tracks are shown as grey or black
lines. The left panel shows higher or more restrictive ice conditions as compared to the left.
Thus in the left panel, some vessel paths go around the ice field where as in the right
panel all paths traverse the ice field. Reproduced from [54].
The development of MDA in the Canadian arctic will require the coordination and leveraging of
multiple information sources. As such, the combining of these sources either through fusion (i.e.,
visual or algorithmic) or through mechanisms such as cueing, will be required to fully exploit the
sparse information available for the area. Techniques to mine this limited information set will be
relevant. As well, utilizing our knowledge of the physical environment to limit our information
sets will also be important. For example, using our knowledge of the physical environment to
indicate vessel characteristics that can then be used in the vessel identification process. As well,
environmental information together with vessel characteristics can be used to predict vessel tracks
through ice infested waters. Reports and presentations in this WBE included:
leveraging multiple information sources or different systems: [17, 40, 46]; and
exploiting vessel and environmental characteristics for building awareness: [44, 45, 47-49,
51, 52, 54].
6 Concluding remarks: Assessment & Way Forward
This concluding section will present an assessment of 06EO and suggestions for a way forward.
The assessment component, will consider both the goals and outputs as originally described in the
proposal, as well as a higher level project assessment against the Defence S&T Strategy.
6.1 06EO assessment against proposal
6.1.1 Objective
The objective of 06EO was: “The expected outcome of this work is an improved ability to develop
situational awareness in the Arctic, taking into account the vast and complex littoral
environment, the harsh environmental conditions and the remote nature of Canada’s north. The
effort will enhance Whole-of-Government collaboration and lead to improved CF decision
making in support of Arctic missions.” This was moved forward through much of the work
in 06EO. For example:
using wide-area information for cueing local sensors (Section 5.1.4), which was designed
around the remote nature of Canada’s north
global vessel identification (Section 5.1.5), which could use the harsh environment as a cue
for ship type if the ship was in the arctic
vessel path interpolation in the presence of coastline and ice (Section 5.1.6), which
recognized the complex littoral environment
Some work is not yet mature enough to impact the current SA, but has shown the potential to
improve SA.
6.1.2 Outputs and deliverables
The outputs and deliverables of the original proposal are reproduced below (slight grammatical
alterations):
1. Report on Maritime Situational Awareness (MSA) related information assets important to
Arctic defence and security
2. Capability assessment of MSA tools and data sources in the Arctic
3. Report on activities / WoG coordination in the Arctic
4. Identify interoperability opportunities and lessons learned when dealing with OGDs and
Arctic stakeholders
5. Report on information content and structure requirements for supporting Arctic defence
and security
Deliverables 1 and 2 were stand-alone tasks that were not individually reported on. However, one
could surmise which information assets were considered important to the MIS group by
examining the assets used in the SEDNA research. Deliverables 3 and 4 were achieved through
the activities conducted under 06EO03 (Section 4), whether formally documented or informally
discussed. This is evident through linkages made with MARLANT, JTFN, CCG, etc. The
documents produced under 06EO02 (Section 3.2) helped achieve Deliverable 5.
6.1.3 Technical Merit
Under Technical Merit in the proposal, it was written that “this project will focus on the
information content, distribution, management, and the cross-departmental utilization of the
information. Work will be conducted on the improvement and application of research algorithms
related to combining similar but different information sources, to identify probable vessel track
characteristics, vessel identity, and likelihood of detection or compliant reporting.” This is an
accurate description of 06EO. Breaking down these two sentences, the work can be mapped to
the ideas:
information: Sections 3.1and 4.1
vessel identity: Sections 2.1.2, 5.1.5
likelihood of detection or compliant reporting: Sections 2.1.1, 2.1.2, 2.1.3, 5.1.1, 5.1.4
Therefore, work was moved forward in the areas that were originally anticipated.
6.1.4 Proposal assessment
Despite being cut short by one year, 06EO achieved many of the goals set out in the original
proposal. It moved forward the objective of the work; it produced formal and informal
outputs/deliverables and it encompassed the topics laid out in the proposal’s technical merit
section.
6.2 06EO high level assessment
When this work commenced, it was supporting the Defence S&T Strategy published in 2006 [55].
Since that time, a second Defence Strategy has emerged. As a result, we consider both Strategies
in the following.
6.2.1 Defence S&T Strategy
The Defence S&T Strategy published in 2006 [55] listed numerous important Areas of
S&T Expertise that were to form the focus of research activities; one such expertise being
Command and Control. One of the S&T Challenges under that expertise was 1.4 Information
Fusion and Knowledge Management and Representation.
Challenge 1.4 was written as “it is recognised that the effectiveness of any military or
para-military intervention depends heavily on the comprehension of the situation at hand”. This,
in essence, is situation awareness. The document further states, “The challenge lies in
implementing effective toolsets for creating, analyzing and managing relevant data, information
and knowledge. Efficient exploitation requires organisational practices through which
information and know-how, in their broadest senses, are shared to facilitate operational
effectiveness.” This project attempted to move this challenge forward, choosing the Arctic as the
geographic condition placed on the situation awareness research. Much of the work of
06EO focussed on analyzing, managing and sharing information, in order to achieve MSA.
In the 2013 version (Defence and Security S&T Strategy 2013 [56]), Areas of S&T Expertise are
replaced by Client Requirements. 06EO work would fall under the third requirement: “Enable the
acquisition, sharing and use of critical information in support of situational awareness and
decision-making”. The work done under 06EO supports finding, integrating, analyzing and
understanding multiple sources of data, which supports the third client requirement.
WBEs 06EO02 and 03 lay ground work for enabling the “sharing [of] information in a timely,
seamless fashion with operational partners, including civilians within DND, other government
departments, international allies and non-governmental organizations”. Although not stated
explicitly in the Defence and Security S&T Strategy, sharing is limited by an assortment of issues
including technical (i.e., the system’s ability to transfer information), semantic or information
science (i.e., the system’s ability to understand what is receives), policy (i.e., the mandates and/or
legal ability to share), and cultural (i.e., the willingness to share).
While the work done in this project did not provide any grand advances in situation awareness in
the arctic, it did provide some steps forward that form a foundation for future work. For example,
the research regarding GeoNetwork, trust, MSARI, and the cueing algorithm are just a few
examples of work that could be continued to support the Client Requirements of the current
Defence and Security S&T Strategy.
6.3 Final evaluation
Using the above sections as evidence, we consider the SEDNA project to have successfully
progressed the overarching objective “improved ability to develop situational awareness in the
Arctic”. This progression was made through investigations in data backhaul, algorithm
development, partnership building, and information assessment. Initial proposal claims were for
the most part, achieved, although not always on schedule. Finally, the project activities supported
both the previous and present Defence S&T Strategies.
6.4 Lessons learned
Documenting the lessons learned as a result of a project represents an important contribution to
future projects. As a result, we note two critical lessons that resulted from SEDNA.
1. The personal networking and partnership building that is required to develop new
relationships with other government departments can be very time consuming. Difficulties
can arise when formalizing agreements related to the collaboration activity or data sharing;
when reconciling the activity with the different mandates of the departments; or when
departmental priorities or goals seem to conflict.
2. Onsite experience is required for Arctic research. This may take the form of ship time, a
critical component of the SEDNA investigation conducted during CFAV QUEST trial Q346.
Another example is that when researching algorithms to improve MDA in the north, direct
interaction with those personnel in the north trying to compile MDA is critically important. It
became clear during the visit to JTFN that they have MDA needs that hadn’t been anticipated.
For example, keeping track of adventurers and explorers is an important part of maintaining
MDA in the Arctic. These people are often not reporting in a regulated manner and must be
found through means other than the data feeds entering the RJOCs.
6.5 Looking forward
As a people, Canadians have a connection to the Arctic, with the majority (74%) of Canadians
willing “to invest heavily on securing sovereignty over its Arctic land” [57]. As a department,
DND has a role to play in securing the sovereignty, both through the monitoring for defence
threats and through direct engagement with other government departments.
DRDC supports DND’s role by anticipating immediate and long term defence needs, in all
Canadian defence warfare areas and geographic domains. DRDC accomplishes this by linking
existing and developing new scientific knowledge and technologies to the defence requirement.
However, often lost in these endeavours are the information management and exploitation aspects
that would result in a more complete or end-to-end system—where one end represents
S&T applied to ensure comprehensive and proper collection, and the opposite end represents
S&T applied in the information science realm to enable the storing, retrieving and exploitation of
the information. This is particularly important in the Canadian north, where the utilization of all
available, and notably sparse, information resources will represent an important component of an
effective sovereignty strategy.
Work done in the SEDNA project has the potential to be the foundation of future DRDC research.
For example, GeoNetwork is a promising way to enable sharing of information with OGDs and
non-governmental organizations. MSARI is set up for further research regarding integrating,
analyzing and understanding multiple data sources. GeoNetwork and MSARI are just two
examples of work done under SEDNA that could be leveraged in future information management
and exploitation research. In addition, the work on trust could be applied to improve trust in the
data for both DND and OGD users. Putting into practice the cueing algorithm in the north could
be an important enabler for information acquisition by facilitating the local sensor to turn on only
when a vessel is approaching. These are just a few examples of how SEDNA research can be
leveraged in Canada’s reshaped defence research programs.
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Ex-Filtration (DRDC Atlantic TM 2013-135) Defence R&D Canada.
[3] DIMTPS 3-6-3-3 Satcom LCMM (2012), Telephone conversation.
[4] Radulescu, D. and Hadzagic, M. (2014), Q346 Maritime Domain Awareness Data
Processing, (DRDC Atlantic CR 2013-075) Defence Research and Development Canada.
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FETCH, Chief Defence Intelligence, (Presentation).
[6] Schaub, D. (2012), On Validating Self-Reported Vessel Location: Application of
Statistical Methods to Assessing Automatic Identification System Reports,
(DRDC Atlantic TM 2011-320) Defence R&D Canada.
[7] Green, D., Fowler, C., Power, D. and Tunaley, J.K.E. (2011), VHF Propagation Study,
(DRDC Atlantic CR 2011-152) Defence R&D Canada.
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Peters, D.J. and Stoddard, M.A. (2011), Final Report 11HL: Technologies for Trusted
Maritime Situational Awareness, (DRDC Atlantic TR 2011-093) Defence R&D Canada.
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situational awareness systems with application to AIS, (DRDC Atlantic TM 2011-279)
Defence R&D Canada.
[10] St-Hilaire, M.-O., Mayrand, M. and Isenor, A.W. (2011), Implicit Trust in a Data Model,
[11] St-Hilaire, M.-O. (2010), Determining the consistency of information between multiple
systems used in maritime domain awareness, (DRDC Atlantic CR 2010-025)
Defence R&D Canada.
[12] ST-Hilaire, M.-O. and Isenor, A.W. (2011), Determining the Consistency of Information
between Multiple Subsystems used in Maritime Domain Awareness, From the
NATO Science for Peace and Security Series - E: Human and Societal Dynamics,
NATO Advanced Science Institutes Series.
[13] Stocks, K.I., Neiswender, C., Isenor, A.W., Graybeal, J., Galbraith, N., Montgomery, E.T.,
Alexander, P., Watson, S., Bermudez, L., Gale, A. and Hogrefe, K. The MMI Guides:
Navigating the World of Marine Metadata (online), http://marinemetadata.org/guides
(Access date: October 9, 2014).
[14] Graybeal, J., Isenor, A.W. and Reuda, C. (2012), Semantic mediation of vocabularies for
ocean observing systems, Computers & Geosciences, 40, 120–131.
[15] Volpe (2009), Maritime Safety and Security Information System (online),
http://ais.volpe.dot.gov/home/ (Access date: October 4, 2009).
[16] St-Hilaire, M.-O. and Mayrand, M. (2014), Maritime Situational Awareness Research
Infrastructure (MSARI): Requirements and High Level Design, (DRDC-RDDC-2014-C97)
Defence Research and Development Canada.
[17] Isenor, A.W., Cross, R., Webb, S. and Lapinski, A.-L.S. (2013), Utilizing wide area
Maritime Domain Awareness (MDA) data to cue a remote surveillance system,
In Proceedings of SPIE Security+Defence 2013, 1-11, Dresden, Germany.
[18] Wright, D., Blongewicz, M.J., Halpin, P.N. and Breman, J. (2007), Arc Marine GIS for a
Blue Planet, Redlands, California: ESRI Press.
[19] ISO (2003), Geographic information – Metadata, (ISO 19115:2003(E)) International
Organization for Standardization.
[20] Isenor, A.W. and Spears, T.W. (2014), Combining the Arc Marine Framework with
Geographic Metadata to Support Ocean Acoustic Modeling, Transactions in GIS,
18 (2), 183–200.
[21] Webb, S. and Isenor, A.W. (2012), Comments on the Global Position Warehouse Database
Design Schema, (DRDC Atlantic (MICS) 2900-2) Defence R&D Canada.
[22] DRDC (2013), RCN Maritime Operational Information Management Support, Directed
Client Support, Project Code 01jy08.
[23] Isenor, A.W. (2013), Comments on SOP/TACNOTE Regarding Use of AIS by HMC Ships,
(DRDC Atlantic 2900-2 (MDS)) Defence R&D Canada.
[24] St-Hilaire, M.-O. and Allard, Y. (2012), Investigation of GeoNetwork Opensource,
[25] Isenor, A.W. (2012), Basic User Instructions for GeoNetwork Application, (DRDC Atlantic
(MICS): 2900-2) Defence R&D Canada.
[26] Isenor, A.W. and MacInnis, A. (2012), GeoNetwork Investigation, MARLANT N6
staff, (Presentation).
[27] Isenor, A.W. and MacInnis, A. (2011), GeoNetwork Investigation, RJOC and MSOC staff;
MSOC project, (Presentation).
[28] St-Hilaire, M.-O. and Mayrand, M. (2014), Automatic Publication of a MIS Product to
GeoNetwork: Case of the AIS Indexer, (DRDC-RDDC-2014-C24) Defence Research and
Development Canada.
[29] Isenor, A.W., McIntyre, M. and Webb, S. (2013), Automatic Identification System data set
for helicopter training simulator, (DRDC Atlantic (MICS) 2900-2 (Project 06EO))
Defence R&D Canada.
[30] Isenor, A.W. and Lapinski, A.-L. (2013), Brief to JTFN, Deputy Commanding Officer
JTFN, (Presentation).
[31] Isenor, A.W. and Lapinski, A.-L.S. (2013), Maritime Decision Support Information
Gathering with JTFN, (DRDC Atlantic (MDS) 2900-2 (Project 06EO))
Defence R&D Canada.
[32] Isenor, A.W., Webb, S.P. and Lapinski, L. (2012), Simulating Detection of Northern Ship
Traffic for Canadian Security, (ARP 11ho SEDNA), IMSWG Committee, (Presentation).
[33] Isenor, A.W. (2012), Use of northern ship traffic simulator for Mary River region,
(DRDC Atlantic (MICS): 2900-2) Defence R&D Canada.
[34] Lapinski, A.-L.S., Webb, S. and Isenor, A.W. (2013), Simulating surveillance
options for the Canadian North, Maritime and Arctic Security and Safety
Conference (MAS), (Presentation).
[35] Isenor, A.W. and Webb, S.P. (2012), ARP 11ho: Situational Information for
Enabling Development of Northern Awareness (SEDNA), Canadian Liaison to NORAD,
(Presentation).
[36] Isenor, A.W. (2012), ARP 11jo: Update to SEDNA, Canadian Liaison to NORAD,
(Presentation).
[37] Hazen, M. and Isenor, A.W. (2012), “Big Data” – Information Management in a time of
global data feeds, TTCP MAR TP-1, (Presentation).
[38] Radulescu, D. (2014), AIS Indexer User Guide, (DRDC-RDDC-2014-C20)
Defence Research and Development Canada.
[39] Isenor, A.W. (2013), Arctic Maritime Domain Awareness, Canadian Forces Warfare
Centre, (Presentation).
[40] Lapinski, A.-L.S. (2014), LRIT and AIS: An analysis of October 2010 data,
(DRDC Atlantic TM 2012-234) Defence Research and Development Canada.
[41] AGI (online), http://www.agi.com/default.aspx (Access date: 20 October 2014).
[42] Peori, K., Thibodeau, M. and Gingell, M. (2014), High Fidelity Simulated Data System
Functional Design Document, (DRDC Atlantic CR 2011-089) Defence Research and
Development Canada.
[43] Frawley, W.J., Piatetsky-Shapiro, G. and Matheus, C.J. (2013), Knowledge Discovery in
Databases: An Overview, AI Magazine, 13 (3), 57–70.
[44] St-Hilaire, M.-O. and Hadzagic, M. (2013), Information Mining Technologies to Enable
Discovery of Actionable Intelligence to Facilitate Maritime Situational Awareness:
I-MINE, (DRDC-RDDC-2014-C96) Defence Research and Development Canada.
[45] Hadzagic, M., St-Hilaire, M.-O. and Webb, S. (2013), Maritime Traffic Data Mining
Using R, In Proceedings of 2013 16th International Conference on Information Fusion
(FUSION), Istanbul, Turkey.
[46] Isenor, A.W., Cross, R., Webb, S. and Lapinski, A.-L.S. (2013), Utilizing wide area
Maritime Domain Awareness (MDA) data to cue a remote surveillance system, SPIE
Security+Defence 2013, Dresden, Germany, (Presentation).
[47] Schaub, D. (2014), Joint Identification of Multiple Tracked Targets, (in process) Journal of
Advances in Information Fusion.
[48] St-Hilaire, M.-O., Audet, O. and Lavallee, M. (2014), Investigation and Implementation of
Matrix Permanent Algorithms for Identity Resolution, (DRDC-RDDC-2014-C291) Defence
Research and Development Canada.
[49] Schaub, D. (2012), In-Memory Analysis of Maritime Data Sets,
(DRDC Atlantic TM 2013-211) Defence R&D Canada.
[50] Dunbar, M.J. and Adams, P. (2014), Arctic Archipelago (online), The Canadian
Encyclopedia, http://www.thecanadianencyclopedia.ca/en/article/arctic-archipelago/
(Access date: 14 November 2014).
[51] St-Hilaire, M.-O., Radulescu, D., Hammond, T. and Lefebvre, E. (2013),
An Interpolation System for Position Report Databases, (DRDC Atlantic CR 2013-137)
Defence R&D Canada.
[52] Radulescu, D., St-Hilaire, M.-O. and Hammond, T. (2014), Interpolation System Software
Enhancements, (in process) OODA Technologies Inc.
[53] Hammond, T. and Peters, D.J. (2009), Probabilistic interpolation between position reports,
From the NATO Workshop on Data Fusion and Anomaly Detection for Maritime
Situational Awareness, La Spezia, Italy.
[54] Hammond, T. (2014), Applications of Probabilistic Interpolation to Ship Tracking,
In Proceedings of 2014 Joint Statistical Meetings - American Statistical Association,
Boston, Massachusetts.
[55] Department of National Defence (2006), Defence S&T Strategy; Science and Technology
for a Secure Canada, 50.
[56] Department of National Defence (2013), Science and Technology in Action: Delivering
Results for Canada’s Defence and Security, 29.
[57] Angus Reid Strategies Canadians Concerned about Russia; Want Action on Arctic
Sovereignty (online), http://www.visioncritical.com/wp-
COTS Commercial-of-the-shelf
DRDC Defence Research and Development Canada
DSTKIM Director Science and Technology Knowledge and Information Management
GCCS-M Global Command and Control System-Maritime
GPW Global Position Warehouse
GUI graphical user interface
HOTEF Helicopter Operations, Training and Evaluation Facility
IM Information Management
IS Interpolation System
ITU International Telecommunications Union
LRIT Long Range Identification and Tracking
MAS Maritime & Arctic Security & Safety (Conference)
MDA Maritime Domain Awareness
MICS Maritime Information and Combat Systems (Section)
MIKM Maritime Information and Knowledge Management (Group)
MIS Maritime Information Support (Group)
MIW Maritime Information Warfare
MSSIS Maritime Safety and Security Information System
S&T Science & Technology
SOP Standard Operating Procedure
TACNOTE Tactical Notice
US United States
VDR Voyage Data Recorder
WBE Work Breakdown Element
WoG Whole of Government
DOCUMENT CONTROL DATA (Security markings for the title, abstract and indexing annotation must be entered when the document is Classified or Designated)
1. ORIGINATOR (The name and address of the organization preparing the document.
Organizations for whom the document was prepared, e.g., Centre sponsoring a
contractor's report, or tasking agency, are entered in Section 8.)
DRDC – Atlantic Research Centre Defence Research and Development Canada 9 Grove Street P.O. Box 1012 Dartmouth, Nova Scotia B2Y 3Z7 Canada
2a. SECURITY MARKING (Overall security marking of the document including
special supplemental markings if applicable.)
UNCLASSIFIED
(NON-CONTROLLED GOODS) DMC A REVIEW: GCEC DECEMBER 2012
3. TITLE (The complete document title as indicated on the title page. Its classification should be indicated by the appropriate abbreviation (S, C or U) in
parentheses after the title.)
Final report 06EO: Situational Information for Enabling Development of Northern Awareness (SEDNA)
4. AUTHORS (last name, followed by initials – ranks, titles, etc., not to be used)
Isenor, A.W.; Lapinski, A.-L.S.
5. DATE OF PUBLICATION (Month and year of publication of document.)
June 2015
57
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01da Maritime Information Warfare
which the document was written.)
10a. ORIGINATOR’S DOCUMENT NUMBER (The official document
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DRDC-RDDC-2015-R085
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