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Device-Free Localization

Ossi Kaltiokallio

Department of Automation and Systems Technology Aalto

University School of Science and Technologywww.autsys.tkk.fi

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Overview

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• Device-free localization (DFL) based on distributed processing of RSSI • Objective • What enables the technology?• How a person can be detected and localized? • DFL application briefly• Experimetal results and DEMO(s)

Objective

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• Device-free localization (DFL) system• Re-deployable, remotely configurable, and easy to use

• Operates in real-time• Target position is estimated with minimal latency

• Distiributed processing of RSSI • Communication overhead is minimized• Limited resources of the node• Enables large scale WSNs

• Energy efficiency• Radio management

Electromagnetic spectrum

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What enables the technology?

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Sources of RSSI variability

• multipath fading and shadowing, antenna differences, node orientation, surrounding environment, distance, transmission power, etc.

Sources of Information (1/3)

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• Fading, deviation of the attenuation • Fading may either be due to multipath propagation or shadowing • Presence of reflectors around communication link

• creates multiple paths that TX can traverse• result RX ”hears” multiple copies of the transmitted

signal, each propagated via different path

• Each signal experiences difference in attenuation, delay and phase shift

• Result can be either destructive or constructing

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Sample Number [n]R

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Sources of Information (2/3)

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• RSSI measurements of one link ...

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Sources of Information (3/3)

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• ... and the link next by, RSSI measurements are quite different

Positioning via RSSI…

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• Single link• The distributed algorithm is capable of detecting LoS

crossings• Accuracy very limited• Possible false alerts• What if accuracy is required? the key lies in nodes

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• Multiple links• Many nodes simultaneously can detect the person• Data aggregation coordinates of the person can be

estimated if nodes positions are known a priori• False alerts can be filtered out

Master’s thesis approach (1/2)

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Master’s thesis approach (2/2)

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Application in a nutshell

DFL System

WSN Matlab

Communication

Distributed algorithm

Positioning Tracking

SITUATION AWERNESS

SINK

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Results of Master’s thesis

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Problems and Solutions

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• Algorithm designed to only detect LoS crossings that are caused by shadowing

• Increse sensitivity multipath fading events also detected

• LoS crossings visualized with a line • The person affects the RSSI also elsewhere model

alerts with an ellipsoid

• Transmission interval (16 ms)• Operations of the nodes optimized (10 ms) more

measurements, better results

Alert model

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… and the story continues

• Each alert is visualized with an ellipsoid• Ellipsoid’s tend to cluster around the actual position

• Why not just send all RSSI measurements to the sink?• Tracking accuracy enhanced with a Kalman filter

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x coordinate [m]

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Known path Estimated path Nodes

Experimental results (1/3)

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Experimental results (2/3)

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Test 1a Test 2c Test 7a Test 3a

Node interval [m] 1 1.5 1.5 2

Monitored area [m2] 16 36 36 (obstructed)

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RMSE 0.19 0.20 0.22 0.23

Min RMSE 0.17 0.17 0.20 0.19

Max RMSE 0.22 0.21 0.23 0.26

Max error 0.63 1.07 1.19 1.14

Alerts [%] 16.43 14.56 16.35 11.69

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Transmission interval [ms]

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Radio ON-OFFRadio always ON

• Demo • Processing the RSSI in a distributed fashion reduces

communication overhead– Alerts account only for around 10-17 % of the total

number of packets (area always occupied)

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• Radio management:– Radio enabled for

TX: 2.30 ms, RX: 4.05 ms– An additive increment in

network lifetime

Experimental results (3/3)