CINTAL - Centro de Investigacao Tecnologica do AlgarveUniversidade do Algarve

SiPLAB internal report

Detection and localization

of a metal target,

using data from a lake experiment

Florent Brechet and Charlotte Tugaye

Rep. 01/01 - SiPLAB10/November/2014

University of Algarve tel: +351-289244422Campus de Gambelas fax: +351-2898642588005-139, Faro, cintal@ualg.ptPortugal http://www.cintal.ualg.pt/

Work requested by CINTALUniversidade do Algarve, Campus de Gambelas,8005-139, Faro, Portugaltel: +351-289244422, cintal@ualg.pt, www.cintal.ualg.pt

Laboratory performing SiPLAB - Signal Processing Laboratorythe work Universidade do Algarve, FCT, Campus de Gambelas,

8000 Faro, Portugaltel: +351-289800949, info@siplab.fct.ualg.pt,http://www.siplab.fct.ualg.pt

Projects SiPLAB internal reportTitle Detection and localization of a metal target, using data from

a lake experimentAuthors Florent Brechet and Charlotte TugayeDate November 10, 2014Reference 01/14 - SiPLABNumber of pages 52Abstract This internal report describes target detection

and localization based on predictions of theTRACEO ray tracing model.

Clearance level UNCLASSIFIEDDistribution list CINTAL(1), SiPLAB(1), FCT(1)Total number of copies 3 (three)

Copyright Cintal@2014

III

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IV

Detection and localization of a metal target,using data from a lake experiment.

PFE reference: ASM02

Students: EV2 F. Brechet and C. Tugaye, EN2012

Type of report: Report of Projet de Fin d’Etude (PFE)

Laboratory: SiPLAB - Signal Processing Laboratory, Universidade do Algarve, Faro,Portugal

Project’s tutor: Dr. Orlando Camargo Rodrıguez

RESUME

En 2007, une experience a ete menee en Norvege dans le cadre de la recherche pour le ren-forcement de la securite des ports. Appelee UAB’07, pour ”Underwater Acoustic Barrier”(barriere acoustique sous-marine), elle a pour but de detecter une cible traversant cette barrieregrace la propagation des ondes acoustiques sous-marines. Cela n’avait pas ete possible. L’objetde ce rapport est de presenter comment les donnees acoustiques de cette experience ont eteutilisees pour detecter et localiser la cible utilisee. Cette etude est aujourd’hui possible suite ala creation en 2010 d’un modele de trace de rayon, cTraceo. De plus, il etait necessaire d’avoirla bathymetrie du lieu pour utiliser ce modele. A partir d’une figure presente dans le rapportde l’experience de 2007, les donnees ont ete extraites et il etait alors possible de detecter lacible en comparant les donnees experimentales aux resultats theoriques. Ce rapport propose,dans un premier chapitre, une courte presentation de l’experience. Ensuite sont exposees lescaracteristiques de la cible (physiques et acoustiques) necessaires a la modelisation ainsi que lademarche utilisee pour obtenir les donnees bathymetriques. Le troisieme chapitre presente lesresultats des simulations effectuees qui seront ensuite compares a ceux obtenus par l’exploitationdes donnees experimentales. Enfin, une methode de detection et de localisation de la cible seraproposee.

ABSTRACT

The UAB project was started by SiPLAB in 2006 to study, develop and test in the field theconcept of underwater acoustic barriers (UABs). The project goals aimed, in particular, to thesecurity of harbour environments in order to avoid threads posed by underwater intruders. Aspart of the project it took place in Norway in 2007 a lake experiment, in which target detectionwas tested through signal de-focalization; target detection proved however to be elusive, namelybecause of technical problems with the receiving array, sinchronization issues and lack of detailedinformation regarding the lake bathymetry. The main goals of this report are to provide acompact review of the lake experiment, and to discuss target detection and localization usingsingle hydrophone data and model predictions provided by the TRACEO ray tracing model. Tothis end bathymetry provided in a bitmap figure was converted into numerical data, allowing areliable representation of the positions of both the source and receiving arrays. Model simulationswere further developed for the transect connecting both arrays in order to provide reliablepredictions of signal transmissions; those simulations were important to support the processingof acoustic data, after which target detection and localization was developed.

Keywords: Ray Tracing, Detection, Localization, Underwater Target, TRACEO Model

SiPLAB PRESENTATION

The present report is the result of the internship project of F. Brechet and C. Tugaye,which were hosted at SiPLAB (Signal Processing LABoratory)1 during 12 weeks. SiPLABis a research center located at the University of Algarve, Faculty of Sciences and Tech-nology, Gambelas Campus, in Faro, south of Portugal; it is constituted by a group ofUniversity professors, researchers scientists and Ph. D. students, interested in signal pro-cessing, underwater acoustics and communications. It currently hosts 11 members, 5 ofwhich are permanent, among with the project tutor, O. C. Rodrıguez2. SiPLAB is partof the Signal Processing Group (SIPg) of the ISR (Instituto de Sistemas e Robotica)3,with CINTAL4 as its main research and development sponsor.

1Webpage: http://www.siplab.fct.ualg.pt.2Webpage: http://w3.ualg.pt/˜orodrig.3Webpage:http://www.isr.ist.utl.pt4Webpage: http://www.cintal.ualg.pt.

V

ACKNOWLEDGMENTS

We would like to thank Prof. Dr. Orlando Camargo Rodrıguez for his esteemedguidance throughout the project, for the support of our work at SiPLAB, and for hisprecious advices in signal processing and ray tracing, that made possible to achieve thegoals of the internship project.

We are also indebted to the whole SiPLAB’s team for allowing us to process the acousticdata from the UAB’07 experiment, and in particular to L. Maia, A. B. Santos and F.Zabel, for their warm welcome, for their help debugging some of the MATLAB codesdeveloped for this work, and also for their assistance preparing documents with LATEX.

VI

Contents

List of Figures IX

1 INTRODUCTION 7

2 THE LAKE EXPERIMENT 92.1 The source array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2 The hydrophone array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3 The bathymetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.4 The sound speed profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.5 Target, target crossings and target detection . . . . . . . . . . . . . . . . . 122.6 Transmitted signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3 BATHYMETRY DATA AND TARGET CHARACTERIZATION 163.1 Bathymetry data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.2 Target characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4 SIMULATIONS 214.1 Preliminary ray tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.2 Eigenray calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.3 Timefront calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.4 Boat-related modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5 DATA PROCESSING 265.1 General description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.2 Raw signal data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.3 Spectrograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275.4 Arrival Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

6 TARGET DETECTION AND LOCALIZATION 346.1 Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346.2 Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356.3 Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

7 CONCLUSIONS AND FUTURE WORK 38

A MATLAB code 41A.1 Image to numerical data conversion . . . . . . . . . . . . . . . . . . . . . . 41A.2 Source and receiver positioning . . . . . . . . . . . . . . . . . . . . . . . . 42

B UTM coordinates 43B.1 General description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43B.2 Geographical coordinates to UTM conversion . . . . . . . . . . . . . . . . . 44

VII

VIII CONTENTS

C Perturbation events 46

List of Figures

2.1 A 916C Lubell acoustic source. . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Source array used during the experiment. . . . . . . . . . . . . . . . . . . . 10

2.3 The AOB array at the pier. . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4 AOB array used during the experiment. . . . . . . . . . . . . . . . . . . . . 11

2.5 Lake bathymetry (depths in m). . . . . . . . . . . . . . . . . . . . . . . . . 14

2.6 Sound speed profile. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.7 Estimated target trajectory (dots) and UAB (continuous line), from [1]. . . 15

3.1 Bathymetry colorbar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.2 Bathymetry with color depths. . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3 MATLAB figure of the lake bathymetry: the asterisk indicates the positionof the source array; the dot indicates the position of the AOB array. . . . 18

3.4 Transect between the source and the AOB: source array is represented asasterisks on the left; un-repeated channels of the AOB are represented ascircles on the right; the target is represented as a black rectangle. . . . . . 18

3.5 Boxes and plates fitting the description of the aluminium target (source:WWW ). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.1 Ray trajectories for the source at depth 3 m with a target at: (a) 30 m,(b) 70 m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.2 Eigenray calculations for channel 1 (hydrophone depth 6.6 m), target range20 m and source depth: (a) 3 m; (b) 4.5 m. . . . . . . . . . . . . . . . . . . 22

4.3 Timefront for a source depth between 2 and 8 m, channel 1 (no target). . 23

4.4 Timefronts for a source depth between 2 and 8 m and a target at 20 m rangefor: (a) channel 1 (hydrophone depth 6.6 m); (b) channel 5 (hydrophonedepth 22.6 m). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.5 Timefronts for source at 3 m depth, and for a target range between 20and 80 m, for: (a) channel 1 (hydrophone depth 6.6 m); (b) channel 5(hydrophone depth 22.6 m). . . . . . . . . . . . . . . . . . . . . . . . . . . 24

IX

X LIST OF FIGURES

4.6 Eigenray calculation for the deepest source and channel 1, with the boatat a range of 20 m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.7 Timefronts for the lower source (at depth 4.5 m) and boat range between20 and 80 m for channels: (a) 1; (b) 5. . . . . . . . . . . . . . . . . . . . . 25

5.1 Raw signal at channel 1, dataset No.1. . . . . . . . . . . . . . . . . . . . . 27

5.2 Spectrogram of AOB data for channel 1, between timestamps 16:30:47 and16:31:01. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.3 Arrival Pattern of AOB data for channel 1, between timestamps 16:01:46and 16:02:10. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.4 Arrival patterns ((a)&(b)) and spectrograms ((c)&(d)) of AOB data withtimestamp: (a) 16:28:59; (b) 16:29:35. . . . . . . . . . . . . . . . . . . . . . 30

5.5 APs for AOB data with timestamp 16:01:46 for channel 1 with: (a) Fmin =12.5 kHz and Fmax = 15.5 kHz; (b) Fmin = 12 kHz and Fmax = 15.5 kHz;(c) zoom of (b) showing a temporal spreading of 0.03 s. . . . . . . . . . . . 31

5.6 APs with the second set of chirp parameters for AOB data with timestamp16:05:46 (channel 1). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.7 APs with the second set of chirp parameters for AOB data with timestamp16:27:22 (channel 13). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.8 APs for AOB data with timestamp 16:25:45 (channel 1) for: (a) alignmentof maxima; (b) no alignment. . . . . . . . . . . . . . . . . . . . . . . . . . 33

6.1 AOB data with timestamp 16:01:22 (channel 1), AP and PAP for: (a)source depth 3 m (S = 0.3205); (b) source depth 4.5 (S = 0.3930). . . . . . 35

6.2 Target detection for channels: (a) 1, (b) 5, (c) 9, (d) 13. . . . . . . . . . . 36

6.3 APs for the detection event shown in channel 1. . . . . . . . . . . . . . . . 36

6.4 Similarity calculations for channel 13 with: (a) dataset No. 55; (b) datasetNo. 65. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.1 Transverse Mercator projection (from [10]). . . . . . . . . . . . . . . . . . . 43

2.2 UTM for Norway (from [9]). . . . . . . . . . . . . . . . . . . . . . . . . . . 44

LIST OF FIGURES XI

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Chapter 1

INTRODUCTION

Harbour protection has being since long an important issue, stressed now more thanever due to the elusive nature of terrorism threads. After the bombing with maritimeImprovised Explosive Devices (IEDs) of the USS Cole and MV Limburg in 2000 and 2002NATO developed multiple efforts in order to improve maritime security against terrorism.One of the biggest concerns is to prevent an underwater intruder to enter a harbour. Infact, autonomous underwater vehicles (AUVs) or divers present a serious threat to harboursafety given their small size and weak acoustic signature. In fact, existing technologicalsolutions of target detection are not 100% efficient to detect such kinds of targets inshallow water.

One of the approaches to target detection is based on the concept of an underwateracoustic barrier. A set of source and receiver arrays can create an underwater “net”, whichis perturbed by the presence of a target. Detecting the perturbation allows to detect (andeventually locate) the underwater target. The UAB project was developed by SiPLABregarding this issue in order to study, develop and test in the field an underwater acousticbarrier, using the principle of the acoustic time-reversal mirror (hereafter TRM). To thisend SiPLAB developed in Norway in 2007 the UAB’07 experiment [1], in which an alu-minium box, towed by a wooden boat, posed as a target. Unfortunately, target detectionbased on the TRM was not achieved due to technical problems with the receiving array,sinchronization issues and lack of detailed information regarding the lake bathymetry.

An alternative approach to target detection is to rely on Matched-Field Processing: adetection system tests whether source position is the one expected, and any changes tothe position are considered as detections of one or multiple targets crossing the UAB.Once detection is confirmed a target-capable acoustic model can be used to locate thetarget. A preliminary discussion of such approach, based on simulations, is presented in[2] for the case of a vertical array and different target positions and sizes.

The main goal of this report is to review the acoustic data from the UAB’07 experi-ment, exploring the chances for target detection and localization based on the principle ofchanges in the source position. To this end reliable bathymetry data was produced froma bitmap figure of the experimental site, simulations were performed with the TRACEOray tracing model [3, 4, 5], the acoustic data was carefully processed and target detectionand localization took place through comparisons between experimental and model data.

This report is organized as follows: chapter 2 compactly describes the UAB’07 ex-periment, chapter 3 introduces a characterization of the metal target and describes theconversion of the bitmap figure to numerical data, chapter 4 describes the simulations,

7

8 CHAPTER 1. INTRODUCTION

chapter 5 discusses in detail the processing of the acoustic data and chapter 6 presentsthe results of detection and localization. The conclusions and future work are presentedin chapter 7.

Chapter 2

THE LAKE EXPERIMENT

This chapter introduces a compact description of the experimental activities that tookplace at the end of of the UAB’07 experiment, in which the concept of the UAB wastested. During such activities an aluminium box, posing as a target, was towed froma boat and target detection was expected through signal de-focalization. This chapterdescribes the environment, the equipment used for target detection and the signals thatwere transmitted. Most of the figures are taken from [1].

2.1 The source array

The first component of the UAB was a source array, composed of two 916C Lubell high-frequency acoustic sources (see Figure 2.1) at depths 3 and 4.5 m, at a place wherethe lake depth was 8 m (see Figure 2.2). The two sources operated simultaneously inorder to achieve target detection through signal de-focalization. Such mode of operationrepresents a challenge for target detection, which will be based on the comparison betweenexperimental data and model predictions. This issue will be discussed in detail in chapter6.

Figure 2.1: A 916C Lubell acoustic source.

9

10 CHAPTER 2. THE LAKE EXPERIMENT

Figure 2.2: Source array used during the experiment.

2.2 The hydrophone array

The second component of the UAB was an Acoustic Oceanographic Buoy (AOB) receiverarray composed of 16 hydrophones (see Figure 2.3). The AOB array was located 100 maway from the sources at a place where, depending on tidal variations, the lake depth wasbetween 26 and 28 m (see Figure 2.4). The AOB array was folded in order to accommodateall hydrophones at its given location; signals were digitally recorded sequentially intochannels of data, and stored on data files using a proprietary binary format which allowsto store acoustic and non-acoustic data (such as cruise title, UTC GPS date and time,sampling frequency and number of channels). Unfortunately, at the end of transmissionsit was discovered that for unknown reasons the same data was recorded on sequentialgroups of 4 channels (with the exception of channel 4, which contained a mean of allrecords). Repeated channels are shown in Table 2.1, together with the depth of the firstun-repeated channel.

Repeated channels Hydrophone depth (m)

(1, 2, 3) 6.6(5, 6, 7, 8) 22.6

(9, 10, 11, 12) 7.7(13, 14, 15, 16) 13.7

(4) 18.6

Table 2.1: Channels / hydrophone depths.

2.3. THE BATHYMETRY 11

Figure 2.3: The AOB array at the pier.

Figure 2.4: AOB array used during the experiment.

2.3 The bathymetry

Lake bathymetry was available after the end of the experiment only as the bitmap imageshown in Figure 2.5. The absence of numerical data for the bathymetry prevented, backin 2007, the development of acoustic simulations with a reliable positioning of the sourceand receiver arrays and with a reliable source-receiver transect. Very little is known aboutthe lake bottom, except that it is made of sand in most of the area (up to a depth of 20m), and that it has a muddy structure made up of marine sediments in the deepest parts.The conversion of Figure 2.5 to numerical data will be described in detail in chapter 3,together with the discussion of arrays positioning.

12 CHAPTER 2. THE LAKE EXPERIMENT

2.4 The sound speed profile

Sound speed for the lake was obtained from temperature and salinity records using thewell known Mackenzie formula [6]:

c = 1449.2 + 4.6T − 0.055T 2 + 0.00029T 3 + (1.34− 0, 010T )(S − 35) + 0.016z , (2.1)

where T represents the temperature in ◦C, S is the salinity in ppt and z is depth in m.The sound speed profile (see Figure 2.6) shows a 15 m thick mixed layer, with a slightlyupward refracting profile, followed by a sudden steep thermocline between 15 and 20 m.

2.5 Target, target crossings and target detection

The target was an aluminium plate 1.2×1.2 m2 towed by a rowing boat, and placed at4.4 m below the surface of the lake. Crossings of the UAB took place with the targetperpendicular to the barrier. Unfortunately, visual records of the target above or underwater are not available, neither information regarding the speed of crossings or the even-tual tilting of the target. The most important source of information to understand thecrossings are the corresponding timestamps, which are indicated in Table 2.2, togetherwith the GPS coordinates of the estimated target position, which are shown in Figure2.7. The timestamps indicate a total of 7 crossings, although timestamps for the boatand target clocks are found to be slightly different. The number of crossings is also dif-ficult to understand considering the trajectory shown in Figure 2.7, which indicates only2 crossings, the first at a distance of around 30 m from the source, and the second at adistance of around 10 m.

Crossing # Boat Target

1 16:06:32 16:07:322 16:10:59 16:12:323 16:14:39 16:15:124 16:17:04 16:17:365 16:21:52 16:22:236 16:23:50 16:24:197 16:25:36 16:26:03

Table 2.2: Timestamps of target crossings.

Target detection was expected to be achieved through signal de-focalization based onthe TRM method, in which the signals received on the AOB array are reversed in timeand are sent back to the source array for retransmission via a radio link. In the absenceof a target the AOB “sees” a focalized reception, i.e., the received signal has no echoes;when a target crosses the UAB it creates a perturbation, which de-focalizes the reception.After a careful processing of acoustic transmissions it was concluded in [1] that a cleardetection of the target based on the TRM method was not possible due to two facts: first,the signals from the source array were detected and synchronized before transmissions;second, the repetition of channels at the AOB array prevented an efficient application ofthe TRM method. The issue of target detection will be discussed again in chapters 4 and6.

2.6. TRANSMITTED SIGNALS 13

2.6 Transmitted signals

The signals transmitted by the source array were composed by linear frequency modulated(LFM) chirps, either up or down sweeps with the same parameters, lasting 0,5 s andrepeated every 2 s. There are, however, discrepancies in [1] regarding the description ofsuch parameters: on page 24 of the report the frequency band is indicated as being 3 kHz,with a central frequency of 14 kHz, while on page 34 the frequency band is indicated asbeing in the interval 12 - 15.5 kHz. This discrepancy of parameters will be discussed indetail in chapter 5.

14 CHAPTER 2. THE LAKE EXPERIMENT

Figure 2.5: Lake bathymetry (depths in m).

1470 1475 1480 1485 1490 1495 1500−25

−20

−15

−10

−5

0

Sound speed (m/s)

Depth

(m

)

Figure 2.6: Sound speed profile.

2.6. TRANSMITTED SIGNALS 15

Figure 2.7: Estimated target trajectory (dots) and UAB (continuous line), from [1].

Chapter 3

BATHYMETRY DATA ANDTARGET CHARACTERIZATION

This chapter describes the method used to convert the bitmap image into numerical dataand introduces a detailed characterization of the target. Both target characterization andbathymetry data are of fundamental importance for the development of simulations withthe TRACEO model, described in detail in chapter 4.

3.1 Bathymetry data

The conversion of the bitmap image (see Figure 2.5) into numerical data was performedin three steps:

• First, the colorbar was extracted from the bitmap (see Figure 3.1). The colorbar wasloaded into MATLAB with the imread command, which represents the colorbar as aset of 3 matrices, size 3×N , where N stands for the values of depth, between 0 and31 m. The first matrix contains the intensities of red, the second matrix contains theintensities of green and the third matrix contains the intensities of blue; all intensitiesare given in an interval between 0 and 255. Ideally, a given column of every matrixis expected to contain identical values of intensity; in practice, the values are slightlydifferent. To obtain an average sequence of intensities each matrix was converted toa row, size 1×N , with MATLAB’s mean command; this command, when applied toa matrix, performs an average of every column by default. Thus the 3×N matrixof red intensities was coneverted into a 1 × N row of red intensities, with similarresults for the other two matrices. The three rows were then merged into a singlematrix, size 3×N , where the first column represented the image color correspondingto 0 m, and the last column representing the image color corresponding to 31 m.By simple linear interpolation each color could be related to a given depth in theinterval from 0 to 31 m.

• Second, it was extracted the portion of the image containing only the depth colors(see Figure 3.2). This second image was loaded again into MATLAB with theimread command, which represented the image as a set of 3 matrices, size L×M ,where M is the number of pixels along the horizontal axis and L is the number ofpixels along the vertical axis. Each pixel color can be selected by looking at the

16

3.1. BATHYMETRY DATA 17

(i, j) element of the three matrices, and the correspondence to lake depth could beestablished by looking at the position of the pixel color in the matrix representingthe colorbar.

• Third, it was assumed that the values indicated in the bitmap image correspond toUTM coordinates in the Norway region. Thus, UTM values along the horizontaland vertical axes were obtained by identifying the coordinates at the corners of thebitmap image, and counting the number of pixels along the horizontal and verticalaxes.

The final touch was to convert the GPS coordinates of the source and AOB arrays toUTM coordinates and thus to calculate the corresponding transect, which is needed forreliable simulations. Source and AOB UTM coordinates were slightly corrected in orderto get the proper values of lake depths indicated in the report.

Figure 3.1: Bathymetry colorbar.

Figure 3.2: Bathymetry with color depths.

The final result of numerical conversion is shown in Figure 3.3. The transect connectingthe source and AOB arrays is shown in Figure 3.4. The MATLAB codes of numericalconversion and positioning are presented in sections A.1 and A.2; UTM coordinates arecompactly described in appendix B.

18 CHAPTER 3. BATHYMETRY DATA AND TARGET CHARACTERIZATION

Figure 3.3: MATLAB figure of the lake bathymetry: the asterisk indicates the positionof the source array; the dot indicates the position of the AOB array.

Figure 3.4: Transect between the source and the AOB: source array is represented asasterisks on the left; un-repeated channels of the AOB are represented as circles on theright; the target is represented as a black rectangle.

3.2. TARGET CHARACTERIZATION 19

3.2 Target characterization

In order to provide accurate estimates of arrival amplitudes one needs a characterizationof the metal target. In metals, the compressional and shear speeds can be calculatedusing the relationships [7]

cp =

√√√√ Y (1− υ)

ρ(1 + υ)(1− 2υ)(3.1)

and

cs =

√Y

2ρ(1 + υ), (3.2)

where

• cp is the compressional velocity,

• cs is the shear velocity,

• Y is the Young’s modulus,

• ρ is the metal density,

• υ is the Poisson’s ratio.

Physical properties of Aluminium (from [8]) and corresponding values of cp and cs basedon Eq.(3.1) and Eq.(3.2) are shown in Table 3.1.

Y (GPa) υ ρ (kg/m3) cp (m/s) cs (m/s)

69 0.346 2700 6.35× 103 3.08× 103

Table 3.1: Aluminium physical properties and corresponding values of cp and cs.

For the sake of visual reference different aluminium boxes fitting the description of thetarget are shown in Figure 3.5.

20 CHAPTER 3. BATHYMETRY DATA AND TARGET CHARACTERIZATION

Figure 3.5: Boxes and plates fitting the description of the aluminium target (source:WWW ).

Chapter 4

SIMULATIONS

This chapter is dedicated to the discussion of the target impact on acoustic transmissionsduring the lake experiment. Generally speaking accurate predictions require a three-dimensional target-capable acoustic model, which is currently not available. Instead, theTRACEO two-dimensional model is used expecting that for the conditions of the lakeexperiment three-dimensional effects were mitigated. The results of these simulations arefundamental for a better understanding of the processing of acoustic data, presented inchapter 5, and for the discussion of target detection and localization, presented in chapter6.

4.1 Preliminary ray tracing

Ray calculations for the conditions described in chapter 2 represent the first step tounderstand if detection was possible during the lake experiment. Ray trajectories for theupper source and two different target positions are shown in Figure 4.1.

(a) (b)

Figure 4.1: Ray trajectories for the source at depth 3 m with a target at: (a) 30 m, (b)70 m.

The calculations indicate that the target’s presence generates a shadowing effect in some

21

22 CHAPTER 4. SIMULATIONS

areas of the waveguide, and such shadowing depends on the target position. Apparently,the size of the shadow decreases as the target moves away from the source, implying that(unsurprisingly) the target is easier to detect when it is located closed to the source.

4.2 Eigenray calculations

Eigenray calculations represent the second step in the discussion of target detection. Aneigenray is a ray linking the source to the receiver, thus the information provided byeigenrays allows to address more specific issues, regarding the structure of rays for a givenconfiguration of the source and the receiver. In order to ease calculations (and consideringthat the target can induce the reflection of rays back to the source) the calculations reliedon the proximity method. With this method TRACEO checks if the final range of a raycorresponds to the range of the hydrophone; if the condition is fulfilled TRACEO thenchecks the condition:

|zh − zray| < ε ,

where zh is the hydrophone depth, zray is the ray depth and ε is a user pre-definedthreshold. It this second condition is fulfilled the ray is considered as an eigenray and itis written to the output file.

Eigenray calculations for both sources are shown in Figure 4.2 and reveal that for thetarget placed at a range of 20 m the direct path is not affected. The calculations alsoreveal that in the two cases later arrivals are the most affected by the target’s presence,a particularity of little advantage for detection given their small amplitude. There arealso significant differences in the two cases in the number of rays bouncing on the target,indicating that detection should be easier to achieve using the deepest source.

(a) (b)

Figure 4.2: Eigenray calculations for channel 1 (hydrophone depth 6.6 m), target range20 m and source depth: (a) 3 m; (b) 4.5 m.

4.3 Timefront calculations

Timefronts represent the third logical step in the discussion of target detection. Generallyspeaking, a timefront is a sequence of channel impulse responses along the receiving array.

4.3. TIMEFRONT CALCULATIONS 23

Since a channel impulse response is a sequence of delta-like spikes of amplitude alongreception time the timefronts are calculated from experimental data with arrival patterns(hereafter APs), which correspond to estimates of the channel impulse response, calculatedthrough cross-correlation between the transmitted and received signals. Synthetic APscan be calculated from model predictions, but such calculations usually produce APswith undesired artifacts, which can mask the true dependence of the impulse responseon hydrophone depth. To avoid such artifacts synthetic APs are substituted in thisreport with pseudo arrival patterns (hereafter PAPs), with a PAP given by the followingexpression:

PAP =N∑i=1

|Ai|e−(∆f(t−τi)2 , (4.1)

where Ai and τi are the amplitudes and travel times calculated with TRACEO, and ∆f isa parameter, chosen in order to produce a PAP as similar as possible to the experimentalAP.

Initial timefront calculations for channel 1 are shown in Figure 4.3 for a variable sourcedepth and no target. Red corresponds to the highest amplitudes of the impulse response,while dark blue indicates the absence of signal. The calculations show that the PAPchanges significantly over source depth, exhibiting a progressive spreading of arrivals assource depth increases. Thus, if a given source has a predominant signature in a particularAP its depth should be easy to estimate by searching the best match between the AP andthe different sequences of PAPs, which form the timefront. This issue will be of extremeimportance for the target detection discussed in chapter 6.

Figure 4.3: Timefront for a source depth between 2 and 8 m, channel 1 (no target).

Additional timefronts are shown in Figure 4.4 for channels 1 and 5, for a target placedat a range of 20 m. As expected, the timefronts reveal the presence of many late arrivals,which can be attributed to rays bouncing on the target when the source is placed at a givendepth. Additionally, for certain source depths the PAPs exhibit the existence of “gaps” inthe initial arrivals, a feature of predictions that can be useful for target localization. Thegaps are also easy to detect in channel 1 and seem to be completely absent in channel 5.

Final timefront calculations for the upper source and the target placed along the intervalfrom 20 m to 80 m are shown in Figure 4.5. The calculations reveal that the timefrontapparently has no gaps in channel 1 (making localization unfeasible for any target range),while the timefront at channel 5 exhibits a significant number of gaps, mostly when thetarget is close to the source.

24 CHAPTER 4. SIMULATIONS

(a) (b)

Figure 4.4: Timefronts for a source depth between 2 and 8 m and a target at 20 m rangefor: (a) channel 1 (hydrophone depth 6.6 m); (b) channel 5 (hydrophone depth 22.6 m).

(a) (b)

Figure 4.5: Timefronts for source at 3 m depth, and for a target range between 20 and 80m, for: (a) channel 1 (hydrophone depth 6.6 m); (b) channel 5 (hydrophone depth 22.6m).

4.4 Boat-related modeling

Taken into account the small size of the target additional calculations were developed con-sidering also that the towing boat could pose as a target. The boat size and characteristicwere infered from the general information provided in [1]; compressional and shear speedsin wood can be found in typical textbooks of acoustics and correspond, respectively, to3600 m/s and 3300 m/s. Normally, wood absorbs a very small portion of acoustic energy(around 3 to 5%), thus the absorption coefficient was set to 0. Yet, a significant dissi-pation of energy can be expected due to the splitting of compressional waves into bothcompressional and shear waves at the boat.

Eigenray calculations are shown in Figure 4.6 for the deepest source and channel 1, forthe boat at a range of 20 m. The calculations reveal that unlike the metal target theparticular positioning of the boat above the surface of the water has a reduced impact onthe eigenrays.

4.4. BOAT-RELATED MODELING 25

Figure 4.6: Eigenray calculation for the deepest source and channel 1, with the boat at arange of 20 m.

Additional timefront calculations shown in Figure 4.7 reveal gaps in the reception ofinitial arrivals at particular boat ranges (both close and far to the source), although ingeneral the impact seems to be very weak.

(a) (b)

Figure 4.7: Timefronts for the lower source (at depth 4.5 m) and boat range between 20and 80 m for channels: (a) 1; (b) 5.

Chapter 5

DATA PROCESSING

This chapter describes in detail the processing of the acoustic data, acquired during thelast day of the UAB’07 experiment. The data are unique in the sense that one wayor another they contain information regarding the target crossings of the UAB. Thediscussion presented in this chapter will start with a general description of the data,followed by a discussion of raw signals, corresponding spectrograms and further arrivalpattern calculations.

5.1 General description

The data processed corresponded to 150 datasets of acoustic signals, with a total size ofalmost 6 GB; each dataset was written in a binary format called vla, and contained 24 sof acoustic data stored in a matrix of 16 channels (one channel per hydrophone). Sincethe repetition rate was of 2 s each channel contained 12 snapshots of the transmittedsignal. Channel data could be loaded into MATLAB using a function written specificallyfor the vla format.

5.2 Raw signal data

Acoustic data is recorded at a given hydrophone of the AOB through the transformationof acoustic energy into an electrical current, whose amplitude in electrical units (ampere,mili-ampere, etc.) is unknown. The electrical signal produced through this conversion isalso not calibrated (i.e. it doesn’t have a 0 mean). As a preliminary (and naive) hypothesisof target detection one could expect that signal amplitude will drop every time the targetcrossed the UAB.

An example of raw data from dataset No.1 is shown in Figure 5.1; visual analysis ofthe data does not reveal any drops of amplitude that could be attributed to the targetcrossings; in fact, it is even doubtful that the dataset contains useful acoustic signal atall. Given the huge amout of data that required processing the analysis of raw data wasquickly replaced by the analysis of signal spectrograms.

26

5.3. SPECTROGRAMS 27

Figure 5.1: Raw signal at channel 1, dataset No.1.

5.3 Spectrograms

An spectrogram is a representation of the signal in both time and frequency domain. Thecalculations of spectrograms for all data allowed to identify 70 datasets, containing theup and down chirps used for target detection. The remaining datasets contained justenvironmental noise. An example of a dataset containing down chirps is shown in Figure5.2. Unfortunately, as in the case of raw data, the visual analysis of spectrograms didn’treveal any particularities related to the target crossings. The next step consisted in theanalysis of arrival patterns.

5.4 Arrival Patterns

A arrival pattern is an estimate of the channel impulse response. It is calculated as theenvelope of the Hilbert transform of the cross-correlation between the transmitted andreceived data. Only down chirps were considered for the calculation of arrival patterns.To this end MATLAB code was written in order to

• Separate the 24 s of data into 12 snapshots of 2 s each.

• Calculate the AP for every snapshot.

• Align all 12 APs by their maximum.

The initial set of chirp parameters was considered to be Fmin = 12.5 kHz and Fmax =15.5 kHz. (see section 2.6). An example of an AP for the given set of chirp parameters isshown in Figure 5.3.

The calculation of APs for the datasets containing useful signals revealed sequentialperturbations, noticed as “gaps” in some of the APs (see Figure 5.4). Those gaps werefound in the APs of datasets with timestamps correlated with the crossing timestamps,

28 CHAPTER 5. DATA PROCESSING

Figure 5.2: Spectrogram of AOB data for channel 1, between timestamps 16:30:47 and16:31:01.

and thus were considered as indicators of target detection. It is interesting to notice thatthe corresponding spectrograms didn’t seem to reveal any particularities related to thetarget crossings.

The presence of perturbations in the APs was extremely encouraging and prelimi-nary comparisons to model data were attempted. Unfortunately, important discrepancieswere noticed: the temporal spreading of arrivals in Figure 4.5 is located in the interval[0.06;0.09] s, which spans over 0.03 s, while the temporal spreading of arrivals in the APswas found to be larger, located in the interval [1;1,1] s and spanning over 0.1 s. The rea-son for such discrepancy was attributed to a wrong choice of chirp parameters, and APswere calculated again with Fmin = 12 kHz and Fmax = 15.5 kHz. This second choice ofparameters revealed a smaller temporal spreading, compatible with TRACEO simulations(see Figure 5.5.

The new set of APs calculated with the second set of chirp parameters revealed alsothe existence of isolated signal perturbations (see Figure 5.6).

There are, however, perturbations that do not fit into the “gap” prediction, which canbe expected from the position of the target at different ranges. As shown in Figure 5.7 APperturbations can affect also all snapshots in a complex way, which is perhaps induced bysignal reflections on the towing boat. Unfortunately, simulations based on this hypothesiswere found to be inconclusive.

The full list of perturbation events is too long to be placed here and it is shown inAppendix (C). In order to classify a perturbation as a detection event the following criteriawas applied:

5.4. ARRIVAL PATTERNS 29

Figure 5.3: Arrival Pattern of AOB data for channel 1, between timestamps 16:01:46 and16:02:10.

• The perturbation should appear between the 2nd. and 11th. snapshot.

• The perturbation should look like a gap of the AP (this criteria is compatible withthe hypothesis that the target was blocking the direct path).

A compact list of perturbations is shown in Table 5.1. The black bullets correspond todetection events, which are not correlated with the timestamos of target crossings.

Despite all care taken in the calculation of APs and in selecting the right set of chirpparameters, and despite the correlation of some detection events with the timestamps oftarget crossings, the variety of signal perturbations pointed to a need of careful validationof the data processing. To this end the original MATLAB code was rewritten as follows:

• Transform an entire record of 24 s into a matrix with 12 snapshots, where eachsnapshot contains 2 s of data.

• Calculate the AP for every snapshot.

• Make a zoom of the APs, containing the acoustic arrivals.

Thus, this second strategy eliminated the need for an alignment of AP maxima. Theresults of such processing were striking: as shown in Figure 5.8 all perturbations vanished.This conclusion was found to be disappointing, yet it provided a reliable set of APs, whichwere further used to develop the detection and localization of the target, which is goingto be discussed in the next chapter.

30 CHAPTER 5. DATA PROCESSING

(a) (b)

(c) (d)

Figure 5.4: Arrival patterns ((a)&(b)) and spectrograms ((c)&(d)) of AOB data withtimestamp: (a) 16:28:59; (b) 16:29:35.

5.4. ARRIVAL PATTERNS 31

(a) (b)

(c)

Figure 5.5: APs for AOB data with timestamp 16:01:46 for channel 1 with: (a) Fmin =12.5 kHz and Fmax = 15.5 kHz; (b) Fmin = 12 kHz and Fmax = 15.5 kHz; (c) zoom of (b)showing a temporal spreading of 0.03 s.

32 CHAPTER 5. DATA PROCESSING

Figure 5.6: APs with the second set of chirp parameters for AOB data with timestamp16:05:46 (channel 1).

Figure 5.7: APs with the second set of chirp parameters for AOB data with timestamp16:27:22 (channel 13).

5.4. ARRIVAL PATTERNS 33

Timestamp (h:m:s) Channel 1 Channel 5 Channel 9 Channel 13

16:05:44 / • / /16:07:32 / / / /16:12:32 / / / /

16:15:12 / / detection /16:15:12 / / / /

16:17:32 detection / / /16:17:36 / / / /

16:20:04 / / • /16:21:44 / • / /16:21:56 • / • /16:22:06 / • / •16:22:23 / / / /16:22:24 / / / detection

16:24:19 / / detection /16:24:19 / / / /

16:24:29 / / / •16:26:03 / / / /16:26:07 / / detection /

Table 5.1: Perturbation events for channels 1, 5, 9 and 13.

(a) (b)

Figure 5.8: APs for AOB data with timestamp 16:25:45 (channel 1) for: (a) alignmentof maxima; (b) no alignment.

Chapter 6

TARGET DETECTION ANDLOCALIZATION

The discussion presented in section 5.4 seems to introduce a negative appraisal regardingthe possibility of target detection (and thus of target localization) in the specific conditionsof the UAB’07 experiment. Yet, the fact that AP perturbations were mostly artifactsintroduced by the initial strategy of data processing can not be considered as the finalanswer to the issue. One of the working hypothesis of this chapter is that, despite thesimultaneous operation of both sources, there is a predominance of the signal from oneof the sources in each channel; in other words, different channels should “see” differentsources. If that predominance is identified, one can use model PAPs for the given source todetect sudden changes in source depth at the given channel along transmissions. For thatpredominance to be found one needs first to define a metric of comparison (a similarity)between APs and PAPs for the upper and the lower source. The largest similarity indicateswhich source is predominant in the AP. The definition of similarity is introduced in section6.1; it will be shown in sections 6.2 and 6.3 that the concept of similarity can be used fortarget detection and for target localization.

6.1 Similarity

Similarity between an AP and a model PAP along the time interval [t, t+ ∆t] is defined,heuristically, as

S =

t+∆t∫t

√AP × PAP dt

t+∆t∫tAP dt

. (6.1)

In an ideal case S would exhibit values within 0 and 1, with 0 indicating total mismatch,and 1 indicating a perfect match.

Similarity values for all channels and for the two sources are shown in Table 6.1, whileFigure 6.1 shows the AP and PAP for channel 1, used for the calculation of S. The tablevalues confirm the original working hypothesis: different channels “see” different sources,with channels 1 and 9 “seeing” the source at depth 4.5 m and channels 5 and 13 “seeing”the source at depth 3 m.

34

6.2. DETECTION 35

Channel Source depth 3 m Source depth 4.5 m

1 0.3205 0.39305 0.4854 0.38309 0.2571 0.376113 0.4039 0.3737

Table 6.1: Similarity values for all channels and both sources (AOB data with timestamp16:01:22).

(a) (b)

Figure 6.1: AOB data with timestamp 16:01:22 (channel 1), AP and PAP for: (a) sourcedepth 3 m (S = 0.3205); (b) source depth 4.5 (S = 0.3930).

With the working hyopthesis confirmed the next logical step is to proceed with targetdetection.

6.2 Detection

Target detection consists simply in developing calculations of similarity for each datasetand channel, using the set of source depths described in section 4.3. For a given channeland dataset one calculates S for a sequence of source depths and then selects the maximumof S. If the maximum is the one already found in Table 6.1 it is assumed that the targethas not been detected; a maximum of similarity that corresponds to a completely differentvalue from 3 or 4.5 m is considered to represent an event of target detection.

The results for the 4 channels are shown in Figure 6.2. As expected, source depth as seenby each channel changes suddenly along transmissions. Channel 1 contains, for instance,a single detection event (the corresponding APs are shown in Figure 6.3). Channel 5contains two events and channel 13 contains several events. The events noticed in channel9 are however not considered to correspond to target detection; in fact, the correspondingsource depths are jumping from one transmission to the next, therefore channel 9 wasconsidered to contain no information regarding the target crossings.

The variations of source depth for the different channels are summarized in Table 6.2.It was later noticed that there were interruptions of transmissions in the datasets No.5

36 CHAPTER 6. TARGET DETECTION AND LOCALIZATION

(a) (b)

(c) (d)

Figure 6.2: Target detection for channels: (a) 1, (b) 5, (c) 9, (d) 13.

Figure 6.3: APs for the detection event shown in channel 1.

and No.51; additionally, the timestamp of the dataset No.15 (16:05:22) is prior to thetimestamps of the data crossings. Taking into acount all of the above the only channelconsidered for target localization was channel 13 (see Table 6.3).

6.3. LOCALIZATION 37

Channel 1 5 9 13Depth (in m) 4,4/6,3 3,1/6,9 3,8/4,4 2/4,7 ; 6,6

∆depth (in m) 1,9 3,8 0,6 2,7Dataset 15 51/53 5 ; 22/24 ; 34/35 ; 38 ; 56 5 ; 51 ; 53/57 ; 65

Table 6.2: Summary of changes in source depth.

Target crossing Channel 1 Channel 5 Channel 1316:07:32 / / /16:12:32 / / /16:15:12 / / /16:17:36 / / /16:22:23 / / 16:20:33 to 16:22:3416:24:19 / / /16:26:03 / / 16:25:22 to 16:25:46

Table 6.3: Final summary of detection events.

6.3 Localization

Once detection has been confirmed one can proceed with target localization. Again, sim-ilarity calculations are performed for the dataset for which detection has been confirmed,and with PAPs in which the target is considered to be placed between ranges of 10 and90 m (see section 4.3). The maximum of similarity indicates the target range.

The corresponding results for channel 13 and datasets 55 and 65 are shown in Figure6.4, and both exhibit a maximumat at a target range of 28 m, a value which is compatiblewith the expectation of target crossings shown in Figure 2.7. Moreover, in both cases theflat behaviour of similarity after 70 m indicates that a target can not be located reliablyif placed at a range interval between 70 and 90 m.

(a) (b)

10 20 30 40 50 60 70 80 90

0.3

0.32

0.34

0.36

0.38

0.4

0.42

0.44

0.46

Target range (m)

Sim

ilarity

Channel 13, Dataset #55

10 20 30 40 50 60 70 80 90

0.3

0.32

0.34

0.36

0.38

0.4

0.42

0.44

0.46

Target range (m)

Sim

ilarity

Channel 13, Dataset #65

Figure 6.4: Similarity calculations for channel 13 with: (a) dataset No. 55; (b) datasetNo. 65.

Chapter 7

CONCLUSIONS AND FUTUREWORK

This report presented an approach for the development of target detection and localiza-tion, through the discussion of the experimental data from the UAB’07 experiment, whichtook place in 2007, and through simulations based on the TRACEO ray tracing model.To this end a bitmap image of the lake bathymetry was converted into numerical dataand an accurate positioning of the source and receiving array was possible through theidentification of UTM coordinates in the bitmap image. TRACEO was fundamental forthe development of target detection and localization thanks to the fact that it allows toaccount for underwater targets. Target detection was initially tried by looking at rawacoustic data, signals spectrograms and arrival patterns. Through trial and error the pro-cessing of acoustic data was optimized, and such optimization was found to provide noclues regarding the presence of the target. At such stage the approach as based on com-parisons between experimental arrival patterns and TRACEO predictions. The approach,based on the detection of changes on source depth, was found to provide a reliable detec-tion of the target; once the target was detected a similar approach was used to providereliable estimates of the target range.

The analysis of acoustic data was certainly intensive, but not exhaustive. Among thefuture directions of research one can consider the following:

• The characterization of the metal target was incomplete; thickness is unknown,as well as the target (eventual) tilting. On the other side the metal target wascrossing at a perpendicular to the UAB, while for modeling purposes the target wasconsidered to be aligned along the UAB; such modeling strategy was adopted inorder to ease the calculation of eigenrays bouncing on the target.

• The boat speed is unknown. Thus, it is unclear how long it took the target to crossthe UAB.

• The only GPS data available are the positions of the source and receiver arrays.With GPS data of the boat and the target buoy one could validate the accuracy oftarget localization.

• Two-dimensional localization would be of extreme interest to validate the robustnessof the method.

38

39

• In the discussion presented in the previous chapters a single channel and a singlesource were considered one at a time. Future discussions need the development ofarray-based estimates.

• In any harbour environment currents play an important role and can induce thetilting of the source and/or receiving arrays. It will be of interest to consider suchtilting in future discussions of the UAB’07 data.

• A probabilistic mapping of detection, based in detection theory, could provide animportant insight regarding the target locations where it can (or can not) be de-tected.

• Development of similar experiments would be certainly of great interest to validatethe methods discussed in this report.

Bibliography

[1] Jesus S.M., and Silva A., Martins C. and Zabel F. Underwater Acoustic BarriersExperiment. UAB’s 07 Part A: the Hopavagen Bay, SIPLAB,Campus de Gambelas,800 FARO-PORTUGAL, December 2007.

[2] Walid Layouni and Sylvain Nicollet. Matched-field based detection, identificationand tracking of underwater targets. SIPLAB Internal Report 1, SIPLAB, Campusde Gambelas, 800 FARO-PORTUGAL, Septembre 2013.

[3] Rodrıguez O.C. The TRACEO ray tracing program. SENSOCEAN Internal Report 1,SIPLAB, Campus de Gambelas, 800 FARO-PORTUGAL, January 2011.

[4] Ey. Emanuel and Rodrıguez O.C. cTRACEO - User Manual. SENSOCEAN InternalReport 1, SIPLAB, Campus de Gambelas, 800 FARO-PORTUGAL, January 2012.

[5] Rodrıguez O.C., Collis J.M., Simpson H.J. and Ey E. Seismo-acoustic ray modelbenchmarking against experimental tank data, Acoustical Society of America, June2012.

[6] Urick R.J. Principles of Underwater Sound. McGraw-Hill, New York, 1983.

[7] L. E. Kinsler et al. (2000), Fundamentals of acoustics, 4th Ed., John Wiley and SonsInc., New York, USA.

[8] http://en.wikipedia.org/wiki/Aluminium

[9] http://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system

[10] http://en.wikipedia.org/wiki/Transverse_Mercator_projection

40

Appendix A

MATLAB code

A.1 Image to numerical data conversion

==================================================Written by Charlotte Tugaye and Florent Brechet

Supervisor: Orlando C.Rodrıguez18/09/2014 11:56

==================================================

clear all, close allclc

%Loading the colorbar:I = imread(’colorbar.jpg’);Reds = I(:,:,1);Greens = I(:,:,2);Blues = I(:,:,3);averagered = round( mean( Reds ) );averagegreen = round( mean( Greens ) );averageblue = round( mean( Blues ) ); ndepths = length( averageblue );matrixcolorbar = [averagered(:) averagegreen(:) averageblue(:)];depths = linspace(0,31,ndepths);

%Loading the bathymetry:I2 = imread(’bathymetry.jpg’);Reds = I2(:,:,1);Greens = I2(:,:,2);Blues = I2(:,:,3);

[m n] = size( Reds );

for i = 1:mfor j = 1:npixelcolor = double( [Reds(i,j) Greens(i,j) Blues(i,j)] );

41

42 APPENDIX A. MATLAB CODE

pixelmatrix = ones(ndepths,1)*pixelcolor;difference = pixelmatrix - matrixcolorbar;thenorm = sqrt( sum( difference.*difference, 2 ) );[dummy index] = min( thenorm );matrixdepths(i,j) = depths(index);

endend

A.2 Source and receiver positioning

==================================================Written by Charlotte Tugaye and Florent Brechet

Supervisor: Orlando C.Rodrıguez19/09/2014 10:58

==================================================

matrixdepths = flipud( matrixdepths );

%create the UTM scaleY = linspace(7.0514e+006,7052239,698);X = linspace(5.2662e+005,527495,750);

%plot the bathymetryfigurepcolor( X, Y, matrixdepths ), shading interp, colorbarxlabel(’UTM values in m’);hold on

%plot the source and AOB on the bathymetry[xs,ys] = wgs2utm(63.593103 , 9.540058);[xAOB,yAOB] = wgs2utm(63.593967 , 9.541167);xs = xs + 30; % manual displacement to obtain correct lake depthplot(xs,ys,’k*’);hold onplot(xAOB,yAOB,’ko’);ZS = interp2(X,Y,matrixdepths,xs,ys)ZAOB = interp2(X,Y,matrixdepths,xAOB,yAOB)

%get the distance between source and AOBXrange = xAOB - xs;Yrange = yAOB - ys;range = sqrt(Xrange^2+Yrange^2);xt = linspace(xs,xAOB,101);yt = linspace(ys,yAOB,101);zt = interp2(X,Y,matrixdepths,xt,yt);rt = sqrt( (xt-xs).^2+(yt-ys).^2);

The function wgs2utm.m is available at the MATLAB File Exchange.

Appendix B

UTM coordinates

B.1 General description

The Universal Transverse Mercator (UTM) system is a two-dimensional Cartesian coordi-nate system, which uses a transverse Mercator projection (see Figure 2.1) to map a regionof large north-south extent with low distortion[9].

Figure 2.1: Transverse Mercator projection (from [10]).

The UTM system divides the Earth into 60 zones in latitude from 80◦S to 84◦N, witheach zone having a width of 6◦ in longitude. Zone 1 covers the longitude interval from 180◦

to 174◦ W, and zone numbering increases eastward until zone 60. Each zone is dividedinto 20 latitude bands, where each latitude band is 8◦ high; zones are designated withletters, starting from “C” at 80◦S, and increasing up in the English alphabet until “X” at84◦N, but omitting “I” and “O”. The area considered in this report is in the southwestcoast of Norway, in the grid zone 32V (9◦ of longitude in width) and is shown in Figure2.2.

43

44 APPENDIX B. UTM COORDINATES

Figure 2.2: UTM for Norway (from [9]).

B.2 Geographical coordinates to UTM conversion

The WGS 84 spatial reference system describes the Earth as an ellipsoidal along north-south axis, with an equatorial radius (the distance from Earth’s center to the equator)given by

a = 6378.137 km ,

and an inverse flattening (the ellipticity of the earth) given by

1

f= 298.257223563 .

To convert geographical coordinates to UTM one needs first to consider the followingdefinitions (distances should be given in km):

n =f

2− f, A =

a

1 + n

(1 +

n2

4+n4

64+ · · ·

),

α1 =1

2n− 2

3n2 +

5

16n3, α2 =

13

48n2 − 3

5n3, α3 =

61

240n3 ,

β1 =1

2n− 2

3n2 +

37

96n3, β2 =

1

48n2 +

1

15n3, β3 =

17

480n3 ,

δ1 = 2n− 2

3n2 − 2n3, δ2 =

7

3n2 − 8

5n3, δ3 =

56

15n3 .

Let us represent latitude as ϕ and longitude as λ. For the conversion to take place oneneeds to apply the following formulae:

t = sinh

[tanh−1 sinϕ− 2

√n

1 + ntanh−1

(2√n

1 + nsinϕ

)],

B.2. GEOGRAPHICAL COORDINATES TO UTM CONVERSION 45

ξ′ = tan−1

[t

cos(λ− λ0)

], η′ = tanh−1

[sin(λ− λ0)√

1 + t2

],

σ = 1 +3∑j=1

2jαj cos (2jξ′) cosh (2jη′) , τ =3∑j=1

2jαj sin (2jξ′) sinh (2jη′) .

The final expressions become

E = E0 + k0A

η′ + 3∑j=1

αj cos (2jξ′) sinh (2jη′)

, (A.I.1)

N = N0 + k0A

ξ′ + 3∑j=1

αj sin (2jξ′) cosh (2jη′)

, (A.I.2)

k =k0A

a

√√√√{1 +(

1− n1 + n

tanϕ)2}

σ2 + τ 2

t2 + cos2(λ− λ0), (A.I.3)

and

γ = tan−1

(τ√

1 + t2 + σt tan(λ− λ0)

σ√

1 + t2 − τt tan(λ− λ0)

)(A.I.4)

By convention, N0 = 0 km in the northern hemisphere and N0 = 10000 km in the southernhemisphere; additionally, k0 = 0.9996 and E0 = 500 km.

Appendix C

Perturbation events

Target detection based on the initial choice of signal parameters is shown in detail in TableC.1; the first column corresponds to the time recorded in the VLA file, which correspondsto the start of recording. Since each set of data lasts 24 seconds the time is not the timeof detection; to obtain the detection time one needs to add 2 s × snapshot number tothe time indicated in the first column. All perturbations are shown in black and blue;perturbations, that fit into the “gap” prediction are considered as target detections and areshown in blue; perturbations unsupported by modeling are shown in black; UAB crossingsare indicated in red. Weak events (but still perturbations) are shown in boldface.

46

47

VLA start of record Channel (1) Channel (5) Channel (9) Channel (13)

16:03:22 / 11 / /16:04:10 / / 1 to 6 /16:05:22 3 9 12 11 / /16:05:46 2 3 / / /16:07 32 / / / /16:12 32 / / / /16:12:59 / / / 10 11 1216:13:22 / / / 2 7 1016:13:46 / / / 116:14:09 / / / 7 10 1116:14:34 / / / 2 316:14:58 / / 7 9 2 4 5 6 7 816:15:12 / / / /16:15:21 / / 8 /16:16:58 / / 4 5 6 7 /16:17 36 / / / /16:19:21 / / 10 /16:19:46 / / 9 /16:21:22 / 11 / /16:21:46 5 3 4 5 6 7 11 5 10 1116:22:10 / / / 7 916:22:23 / / / /16:22:34 / / 1 2 316:22:58 / / 11 /16:23:23 / / 4 7 8 9 12 4 8 9 10 1216:23:46 / / 2 2 3 5 916:24:10 / / 4 5 9 1016:24:19 / / / /16:24:34 / / / 2 5 8 9 10 1116:24:58 / / 10 11 3 516:25:22 / 11 1 2 6 12 5 6 7 8 9 10 11 1216:25:45 / / 1 2 3 4 5 6 11 1 2 4 5 6 7 8 916:26:03 / / / /16:26:10 / / / 2 11 1216:26:34 / / / 3 4 616:26:58 / / / 10 1116:27:22 / / / 1 4 6 916:27:46 / / / 7 11 1216:28:09 / / 6 3 9 1016:28:34 / / / 4 5 7 1116:28:59 / / / 1 216:29:35 3 4 5 1 2 3 4 5 6 1 2 3 4 5 2 4 6 7 8 9 1016:30:00 / / / 1 3 616:30:24 / / / 1216:30:47 / / 7 8 9 10 216:31:13 / / 1 2

Table C.1: VLA start of record and snapshot detection for the different channels.