Localisation radio, indoor et WBAN Le point de vue de la propagation

Pr. Bernard Uguen

Séminaire COMELEC

Télécom Paris Tech , Jeudi 5 Février 2015

Outline

Introduction IR-UWB technology for localisation Heterogeneous networks Cooperative localization Multipaths view as an asset for localisation Localization and WBAN Localisation and Maps The PyLayers platform (demo) Conclusion

Localisation, pour quoi faire ?

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• Augmented reality overlay, gaming, location aware applications • L’information de position apparait également utile dans de nombreux problèmes

d’optimisation de réseaux (D2D, SON,MU-MIMO…) • Pour ce qui est de la localisation indoor, il n’y a pas une solution standardisée, mais une

profusion de techniques variées avec une infrastructure plus ou moins lourde : • Localisation hétérogène • Localisation coopérative • Localisation à base d’IMUs • Fusion de données

Definition : Localization vs Positioning

• Positioning – A (node,terminal,agent,UE) is seeking for its own position – GPS does positioning – A reflexive relation (self-localization)

• Localization – An infrastucture localizing a set of UE – A node seeking for the position of an other node – Radar does localization – A mutual relation

• Navigation – A target exists – Time comes into the game – Positioning is a sub-function of the navigation function

• Localization can help positioning and vice-versa – Provided a communication channel exists

Traces radios RSSI + GPS (Rennes)

Toward Ubiquitous Indoor Positioning – How ?

It doesn’t exist yet a fully satisfying solution for indoor positioning

How to make an asset from multipaths ?

Time variability of the relevant information

Heterogeneity of data sources

Heterogeneous networks are an asset : ( WiFi, UMTS/LTE, UWB,...)

Denser Networks, Wider bandwidths

Non RF resources ( IMU, …)

Position is Relative. Positioning needs Relations

O

TOA vs TDOA

Trilatération hyperbolique Trilatération

Ces techniques à base de ranging s’opposent aux techniques à base de fingerprinting (cloud oriented – crowd data collection – requete vers une base de données)

Au sein du réseau : qui (veut|peut) forger la position ?

Fingerprinting Localization – User profiling - Big data

CellID – RSCP –OTDOA – Fingerprinting

GNSS (outdoor) + Heterogeneous Cooperative localization (indoor)

WWW WWW WWW WWW WWW

A Broad Range of Applications

User-centric applications in GPS-denied environments

Monitoring/smart inventory of personal goods from a smartphone

Context-aware indoor navigation

Security, Safety, Medical

Sensor networks, IoT, interaction with objects (WBAN)

Expected large impact comparable to that of GSM cellular communications

Optimizing telecommunication networks

Localization allows anticipation and network self organization

Transportation Systems

GreenNet

2 Radio Localisation related Projects

FP7 project WHERE 2 (Wireless Hybrid Enhanced Mobile Radio Estimators): The WHERE2 project is a successor of the WHERE project and addresses the combination of positioning and communications in order to exploit synergies and to enhance the efficiency of future wireless communications systems.

• How available heterogeneous resources (WiFi, UMTS/LTE,UWB,…) can cooperate to provide an accurate position estimation in indoor environment ?

• How position estimation can benefit both to the end user (EU) and/or to the infrastructure ?

ANR CORMORAN:

The CORMORAN project has been focusing on new forms of cooperation in and between wireless body area networks (WBAN). Targeted applications include coordinated navigation of groups in buildings or large-scale gesture recognition for gaming, sports and healthcare.

07/2010 -> 06/2013

01/2011 -> 06/2015

IR–UWB technology

What tells us the Ranging CRLB ?

High accuracy ranging requires Ultra Wide Bandwidth signal

S.Gezici and al, « Localization via Ultra Wideband » Radios, IEEE Signal Processing Magazine JULY 2005

IR-UWB technology enables Indoor Localization and Positioning

Shape of the IR-UWB (IEEE.802.15.4a) transmitted pulse

IEEE 802615.4a-2007 p 82-83 IEEE 802615.4a-2007 p 82-83 IEEE 802615.4a-2007 p 82-83

Channel {0:3, 5:6, 8:10, 12:14}

UWB Regulation UWB Systems, Spectrum Regulation and Normalization

Power density mask is restrictive.

Limit the range Frequency plan PHY-UWB IEEE

802.15.4a standard

[ Source ] IEEE 802615.4a-2007 p 82

IR-UWB Received Waveforms in Indoor Environment

Two Way Ranging procedure (TWR)

RDEV : Ranging capable Device PHR : PHY Header - ranging bit

RFRAME : A ranging frame has a PHR ranging bit set to 1. Those frame can simultaneously carry data.

The time reference instant is fixed with the first impulse of the PHR RMARKER

Two Way Ranging procedure (TWR)

- = 2× TOF

The useful information is distributed

Two Way Ranging got poor accuracy

t p− t p≈1

2× t treplyB× (eA− eB)

1ns ~ 30cm

Symmetric Double Side Two Way Ranging (3 Way Ranging)

t p− t p≈1

2× Δreply× (eA− eB)

Δreply =treplyB− t replyA

Improve ranging precision with poor quality clocks

Ranging with non Coherent Receiver How to mitigate the TOA bias ?

[source] Ismail Guvenc and Zafer Sahinoglu « Threshold-Based TOA Estimation for Impulse Radio UWB Systems » Ultra-Wideband, 2005. ICU 2005. 2005 IEEE International Conference on, 420-425

Leading edge detection error : Rx1

Leading edge detection error : Rx2

Leading edge detection error : Rx3

Leading edge detection error : Rx4

Mitigation of location-dependent micro-shadowing effects on TOA due to metallic screens, based on detailed maps Highly localized metallic furniture pieces cause the strongest TOA estimation errors (more than within simpler LOS/NLOS classifications)

3-step map-aided algorithm • 1) Determine a priori radio “screening” regions from the

detailed map (possibly with cumulative effects, depending on the local nb of screens)

• 2) Under mobility, perform coarse LS to pinpoint the mobile on the map and retrieve location-dependent TOA error statistics

• 3) Apply the retrieved statistics in a refined WLS step (possibly coupled with subsequent tracking filters)

Map-Aided Localization (CEA)

Dynamic TOA estimates along

a trajectory

(with highly localized &

cumulative screening effects)

Map-aided WLS compensation of

local TOA errors due to screening

effects

Main players for Ics impulse radio devices

PinPointer 5cm @500m LOS Original architecture Ranging World Record Outdoor in April 2013

ScenSor IEEE 802.15.4a standard compliant 10cm @450m LOS

Ranging bias in NLOS Indoor

Experimental Impulse Radio IEEE 802.15.4a UWB Based Wireless Sensor Localization Technology: Characterization, Reliability and Ranging

True distance 3m (Soft NLOS) True distance 3m (Hard NLOS)

The Generic Receiver « Cracking the IR-UWB synchronization problem »

[Source ] CEA Leti, Grenoble, France

S. Paquelet, L.-M. Aubert, “Method for Detecting UWB Pulse Sequence without Local Pulse Generation”, US Patent n°7,551,891 B2, June 2009

The Generic Receiver Cracking the IR-UWB ranging problem

[Source ] CEA Leti, Grenoble, France

G. Masson, et al., “A 1nJ/b 3.2-to-4.7GHz UWB 50Mpulses/s Double Quadrature Receiver for Communication and Localization”, in Proc. ESSCIRC’10, pp.502-505, Sept. 2010

Taking Advantage of

Devices Cooperation

Cooperative positioning principle

A

B

C

D

A positioned mobile node becomes an anchor

Cooperative positioning

• Cooperation is always good

• But, it comes at a cost of :

– communication overhead

– computational complexity

• Belief Propagation (message passing)

• Factor graphs

• Parcimony

– Link selection

– Censoring

[Source] Hadi Nouredine « Some Signal Processing Techniques for Wireless Cooperative Localization and Tracking » Ph-D Thesis 2012 p 81

– Extending cooperative Non-parametric Belief Propagation (NBP)

to combat geometrical ambiguities, integrate a priori radio information and support mobility

– Ambiguities: Using connectivity model in NBP a posteriori

distributions & messages broadcast (wrt. K-hop neighbors) Two phase (TP)-NBP

– A priori radio info: Incorporating known shadowing maps (with

imperfect knowledge) within RSSI-based NBP positioning – Tracking: Using Particle Belief Propagation, drawing samples from

transition models and applying 2-step message exchanges to update weights (no backward messages)

Cooperative Positioning (MERCE)

Tracking through particle belief propagation with

4 ANs

Enhanced RSSI-based positioning with a priori

shadowing maps only wrt. ANs (besides

cooperative mobiles)

Map-Aided Localization (IETR-UR1)

Enhance the accuracy of geometrical localization approaches by using available prior map information.

Map constraints set in the form of additional

geometric constraints to RGPA Map constraints obtained as prior information

(building layout) or inferred out of on-line statistical learning of the user’s movement

Room 1

-Red rectangle = initial RGPA region where the

target location lies with 3 TOAs.

- Blue dashed rectangle = A priori map

information (“User is in Room1”) reducing the

RGPA search region

Principe : Réduire les erreurs liées à la propagation en sélectionnant le voisin le plus informatif pour du positionnement coopératif

Selection de liens

Identification des ancrage redondants

10 15 20 25 30 35 400

1

2

3

4

5

6

7

Number of nodes

Loca

liza

tion e

rror

(m

)

RMSE

Exhaustive search

Spatial correlation

10 15 20 25 30 35 400

2

4

6

8

10

12

14

16

18

20

Number of nodes

Tim

e(s)

Exhaustive search

Spatial correlation

Différentes stratégies

erreur comparable

Différentes stratégies

Ordre de complexité différent

Maximisé un fonction d’utilité basée sur le GDOP

T

Target

Anchor

Virtual anchor

Hybrid Geometric Positioning

Robust Geometric Positioning Algorithm (RGPA) [1] geometrically resolves the positioning problem by using an iterative space partioning method:

1. Each radio observable describes a geometrical shape which can be bounded by a constraint box.

2. The intersection of all the constraint boxes describe an intersection box, which contains the intersection area.

3. The intersection box is bisects into 4 boxes. Boxes enclosed by the intersection area are kept. Boxes containing or crossing the area are candidate for a new bisection.

4. When enclosed boxes are small enough, the position is estimated as the center of mass of all enclosed boxes.

[1] N. Amiot, M. Laaraiedh, and B. Uguen, “Evaluation of a geometric positioning algorithm for hybrid wireless networks,” Software, Telecommunications and Computer Networks (SoftCOM), 2012 20th International Conference on, pp. 1–5, sept. 2012

1

2

3

4

Heterogeneous Positioning (IETR-UR1)

– Hybrid localization based on a geometric representation of Location Dependent Metrics (LDPs) Robust Geometric Positioning Algorithm (RGPA)

– Observables represented in the form of sets of points using

interval analysis – Computing the intersection of all available observables

through area intersection methods – Final estimates as centroids of intersecting areas – Fusion of observables (radio and non-radio) treated as

geometric constraints within a compact & computationally efficient formalism

Introducing a gradual nb of additional RSSIs on

top of 2 TOAs

ML (Random init.)

RGPA

ML (Perfect init.)

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Hypothesis Testing (HT) Algorithm

With [2] N. Amiot, T. Pedersen, M. Laaraiedh, and B. Uguen, “A hybrid positioning method based on hypothesis testing,” Wireless Communications Letters, IEEE, vol. 1, no. 4, pp. 348–351, 2012.

Taking advantage of Heterogeneous Network

ZIGBEE PLATFORM

1. 2.4 GHz ISM Band

1.Bandwidth: 5 MHz

2.Data rate: 250kbps

3.Tx power: -2dBm

4.30mA@3V

5.Modulation: OQPSK

1. Sensor integration

2. Inercial data

3. Location estimation

4. RSSI data

OFDM PLATFORM

1. 2.4 GHz + 5.2 GHz ISM Band

2. Bandwidth: 20 MHz and 40 MHz

3. Flexible algorithm prototyping /

verification

1. TDoA/RSSI with 3GPP-LTE

2. Round-Trip-Delay for cooperative nodes

MIMO 4G PLATFORM

1. 1.9GHz (0.35-3.9GHz)

2. Bandwidth ≤ 20MHz

3. Up to 4x4 MIMO

4. OFDM (LTE)

5. Carrier aggregation

6. TDD & FDD

1. RSSI, channel impulse

response, Power Delay

Profile (PDP)

2. P Space DP (PSDP)

WHERE2 Platforms

IR-UWB PLATFORM

1. RF/BB 130nm IC

2. 4.5 GHz center freq

3. Bandwidth: 500 MHz

4. DBPSK modulation

5. 1mW Tx / 15mW Rx

6. 802.15.4-like MAC

1. Round-Trip-Delay ranging

(0.3m res.)

2. RSSI, BER, Channel Impulse

Response

3. Inertial sensors data

PTIN Measurement campaign

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• FP7 project WHERE 2 • Measurement campaign realized during 4 days in

April 2013 in the office building of Portugal Telecom (PTIN) in Aveiro

• The measurement platform was composed of various devices, including ZigBee, IR-UWB, LTE, WiFi and inertial units.

Measurement Scenarios

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• Considered scenarios includes: - 15 IR-UWB nodes providing TOF (red dots) - 7 Zigbee nodes providing Power observables (green triangles)

• 2 scenarios have been considered corresponding to 2 different run: - One learning model parameters (18 measurement positions - black dots) - The other for algorithm validation (150 Measurement positions – blue dots)

Learning scenario A Validation scenario B

Model parameters obtention

Mobile-to-

fixed

Fixed-to-fixed

N.AMIOT, "Design of simulation platform joining site specific radio propagation and human mobility for localization applications " pH.D Thesis 2013

Example of RSSI static link

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Static links presents different level related to - The human activity - Modifications in the environment (open/close doors) Extracting a simple model per node is complicated Time variation of RSSI is a rich information

human shadowing

N.AMIOT, "Design of simulation platform joining site specific radio propagation and human mobility for localization applications " pH.D Thesis 2013

Static link – 3 doors involved on the link 23 Different quantified level observed

Positioning error results RGPA: • Similar results between RGPA and ML when

using only ZigBee observables • Good performances of RGPA using only

UWB ToA observables • Low improvement for RGPA when

combining UWB ToA and ZigBee RSSI observables

RGPA HT: • Simulation limited to 2 UWB ToAs

observables and 1 the strongest ZigBee RSSI. • HT slightly ameliorate RGPA performances

in terms of RMSE and std • HT very dependent on the RSSI model

parameters

RMSE(m) Std dev.(m)

Algorithm

LDP ML RGPA ML RGPA

RSSI 3.27 4.01 2.63 2.96

ToA 2.13 1.64 1.31 1.43

RSS + ToA 1.93 1.64 1.56 1.52

RMSE (m) Std

Dev.(m)

ML 2.10 1.98

RGPA 1.85 3.14

RGPA HT 1.70 2.36

Only 2 ToAs and 1 RSSI All availables observables

Taking Advantage of

Multipaths

MINT (Multipath Indoor Navigation and Tracking) The Virtual Anchors (VA) Concept

Paul Meissner, Erik Leitinger , Markus Fruhle, and Klaus Witrisal « Accurate and Robust Indoor Localization Systems using Ultra-wideband Signals » April 2013 Archiv paper

In-Site Radio Parameters Adaptation

– Within parametric RSSI-based localization, path loss model and parameters required a priori with good precision to reflect the environment dynamics

– Ease self-calibration procedures in autonomous

networks

– Define a specific path loss model per reference

node (Macro or FemtoBS) to be updated on-line as the channel changes, using previously estimated locations and raw RSSI measurements

– Focus on the estimation of propagation constants

and shadowing variance (assuming log-normal shadowing model)

Refinement of Rx-specific path loss parameters

Dynamic Rx-specific ranging accurcay

1 BS antenna array (SIMO)

ANR Project CORMORAN

The CORMORAN WBAN measurement campaign: • 3 different radio technologies (HiKoB, CEA

platform and Beespoon phone) • Up to 24 radio devices equipped on a single body • A Precise capture of the radio device and body

positions using a Vicon motion capture (MOCAP) system.

• A perfect knowledge of the capture environement • 58 Series with Capture or Group Navigation

scenarios

One of the main advantage of this campaign is the use of a precise motion capture system which allows to get a ground truth position of any radio device which make the radio observable values open to interpretation.

Example of data exploitation

On-Body RSSI behavior • We can notice the 9 sequences (Subsets 3 and 4) • Cycling slow (static pose - S i ) and fast (walking - M i ) variations

4 subsets of links exhibiting similar behavior associated with Right Wrist (holding a smartphone):

• Subset2: visibility with RW • Subset3: often and strongly shadowed with RW

• Subset4: fast moving terminations with RW • Subset5: links between Left Wrist and nodes HeadRight, TorsoTopLeft, backCenter and ElbowLeft

B. Denis , N. Amiot , B. Uguen , A. Guizar , C. Goursaud , A. Ouni , C. Chaudet . Qualitative analysis of rssi behavior in cooperative wireless body area networks for mobility detection and navigation applications. In 21st IEEE International Conference on Electronics Circuits and Systems (ICECS-2014), 7-10 December 2014.

Handling Body Mobility

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• The mobility model allows to create trajectories which describe the movement of the center of mass of the agents. At each center of mass, it is possible to attach a Body. • Bodies are represented using a multi-cylinders model obtained from a C3D file. The position of each cylinder can be obtained at any time. • Each body can "wear" multiple radio devices (Watch, cell-phone, heart rate monitor,… ).

www.pylayers.org Open Source Indoor Propagation Simulator for Mobile Localization

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• The evaluation of localization algorithms (and communications protocols) should be done under realistic conditions.

• This motivates the development of an open source project PyLayers* which is a simulation platform localization-oriented (incl. UWB-ray tracing, localization algorithms,…).

• In particular, the project handles a Realistic Human Mobility modeling.

PyLayers Simulation Platform

Propagation

Mobility Localization

PyLayers

* Amiot, N.; Laaraiedh, M.; Uguen, B., "PyLayers: An open source dynamic simulator for indoor propagation and localization," Communications Workshops (ICC), 2013 IEEE International Conference on , vol., no., pp.84,88, 9-13 June 2013

Slide 55 > PyLayers: An Open Source Dynamic Simulatorfor Indoor Propagation and Localization> N.Amiot, et al. > ANLN Works. / IEEE ICC’13 - Budapest - 2013, June 9th

Aggregated received UWB waveform with realistic antenna

Less than 1 s/ point of the trajectory

Fast & Realistic LDPs Computation : Graph based Ray-tracing example of PyLayers output

DLR floor plan W2 measurement campaign

Rx

UWB Ray Tracing

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• Raytracing handling Impulse Radio Ultra Wideband Band (IR-UWB) waveforms

• Compact UWB Antenna modeling using spherical harmonics representations

• Expressive syntax for running high level simulation:

>>> from pylayers.simul.link import *

>>> L=Link()

>>> L.eval()

Plateforme Open Source de simulation cross-layer : L’initiative : PyLayers

• La localisation est un domaine éminemment cross-layer • Objectif : accéder à de l’information topo-dépendante à différents niveaux d’abstraction • Ambition : Un simulateur qui s’étende de l’électromagnétisme à la couche applicative par intégration progressive d’outils existant. • Orienté Open Source, Open Data, WBAN, Interactivité et modularité • EM

• OpenESM (FDTD) lien en cours , scikit-RF • GIS

• (SRTM,ASTER,OpenStreeMap,IndoorOSM,…) • MAC

• WSNET (lien en cours) • PHY

• MIMO, IR , OFDM, CDMA, Energy Detector, gnuradio (en projet), real standards • MOB

• Body et CorSer • Simulateur événementiel de trajectoires (Simpy)

• APP • RGPA, Algo localisation, ….

• MES • Sondeurs de canaux MIMO, WHERE1-(M1-M2-M3), CORMORAN

Inter

IntB

IntR

IntT

IntD

Ctilde

Tchannel

Antenna

AntArray

AntSet

SHcoeff

Loss

Coverage

Slab

Mat

Layout Ezone

osmparser Cycles

B U FU

TU

FH FUD

TUD

FUDA

TUDA

Noise

Wafeform

MIMO

Link

Trajectory

Signature

Rays

Body

Devices

Standards

Outdoor and Indoor Maps

Example of GeoData in PyLayers : SRTM EM) +ASTER(DEM)

+OpenStreetMap (Vector)+HDF5 (Rennes Area)

Open Maps for Indoor Location could become available

Indoor floor plan extracted from Open Street Map

My Way (ADP) Android App

Google maps floor plan

+ A josm plugin has been developped for editing and exporting indoor osm layout in PyLayers format

Gr

Gs Gv

GiG

w

Ray-tracing

Mobility

A chain of 6 graphs is derived from the edited layout

Acceleration of ray tracing (Signature) & Pedestrian indoor mobility

>> L.build()

Gt

Pre Processing : Extracting Meta Data from the Indoor Layout

Indoor Mobility Modeling for Pedestrian Users

Gr G

w

Mobility of agents is ensured by independent processes driven by a discrete event simulator (based on SimPy [SimPy])

Mobility Finding a destination + finding a path + moving

Agent destinations are chosen randomly among nodes (rooms)

Inter rooms paths are determined with a shortest path algorithm on

Virtual forces (VF) model ensures agents movement :

1 attractive VF pulling the agent to its destination

Several VF pushing off an agent from walls and peer agents

Gw

Gr

LDPs Computation : Graph based Ray-Tracing

2 key ideas for increasing ray-tracing speed (required for mobility):

Ray-signatures

Graph

Ray-signatures:

Interactions involved in a channel response are globally invariant from a given room to another.

For a small displacement of Tx/Rx, the list of involved interactions (signature) is re-exploitable for producing rays.

Graphs allows to quickly find the signatures.

Gi

5 steps Graph based Ray-tracing:

Ray-signatures rays 3D rays CIR LDPs.

Conclusion

Ubiquitous Indoor localization with GPS-like accuracy is not yet available IR-UWB is a key enabling technology Proper data fusion of heterogenous informations (IMUs, different RAT, maps,...) Using nodes Cooperation – depends on D2D enabled capabilities Highly context dependent situations Enabling non-degraded location in Non-Line-of-Sight situation How to take benefits from multipaths (Virtual Anchors) Self learning techniques (shadowing maps, mobility , path poss models,...) If you are interested, clone or fork, have a try and contribute back to PyLayers