A Distributed Time-Difference of Arrival Algorithm for Acoustic … · 2018-01-10 · P. W. Boettcher, J. A. Sherman, and G. A. Shaw, “Target localization using acoustic time-difference

MIT Lincoln Laborator y

A Distrib uted Time-Diff erence of Arriv alAlgorithm for Acoustic Bearing Estimation

Peter Boettc herGary Shaw

Jeff Sherman

SensIT PI MeetingJanuary 15-17, 2002

This work is sponsored by DARPA under A/F Contract #F19628-00-C-0002. Opinions, interpretations, recom-mendations and conclusions are those of the authors and are not necessarily endorsed by the Department ofDefense.

TDOA-1PWB January 15, 2002

Outline


Motiv ation

Time-Diff erence of Arriv al Algorithm

Time sync hronization

GPS averaging

Reference coherent bearing estimates

Summar y


Motiv ation


Algorithm that produces instantaneous position estimates

Tracker that operates outside the field of sensor s

Algorithm that is invariant to analog gain

Algorithm that assumes no target dynamics and does not constrain targetmotion

Tracker whose accurac y scales with additional sensor s


Outline


Motiv ation

Time-Diff erence of Arriv al Algorithm– TDOA Estimation– Localization algorithms– TDOA Performance Simulations– TDOA Results from SITEX02


GPS averaging


Summar y


Acoustic Time-Diff erence of Arriv al


Estimate dominant frequenc y on each node

Downsample dominant frequenc y series, share among nodes

Cross-correlate to determine time delays

PSfrag replacements

Time

Em

itter

Fre

quen

cy

(Hz)

PSfrag replacements Nod

eN

ode

Time

Time

locationTargetTDOANetwork

communication generationestimation

frequencyestimation

Dominant

samplingAcoustic







PSfrag replacements

Time

Em

itter

Fre

quen

cy

(Hz)

PSfrag replacements Nod

eN

ode

Time

Time



frequencyestimation

Dominant

samplingAcoustic

TDOA-5-aPWB January 15, 2002






PSfrag replacements

Time

Em

itter

Fre

quen

cy

(Hz)

PSfrag replacements

��

� ��

��

Nod

e

�

Nod

e

�

��

� ��

��Time

Time



frequencyestimation

Dominant

samplingAcoustic

TDOA-5-bPWB January 15, 2002






PSfrag replacements

Time

Em

itter

Fre

quen

cy

(Hz)

PSfrag replacements

��

� ��

��

Nod

e

�

Nod

e

�

��

� ��

��Time

Time



frequencyestimation

Dominant

samplingAcoustic

TDOA-5-cPWB January 15, 2002

Bearing solution


Pairwise TDOA exchang e gives two ambiguous bearings

wavefront

wavefront

sensor �sensor ��

�! �"

With several sensor pair s, the correct estimate appear s consistentl y, pic kaverage bearing

PSfrag replacementsPick median bearing

SensorsIncorrect bearingCorrect bearing


Bearing solution


Pairwise TDOA exchang e gives two ambiguous bearings

wavefront

wavefront

sensor �sensor ��

�! �"

With several sensor pair s, the correct estimate appear s consistentl y, pic kaverage bearing

PSfrag replacementsPick median bearing

SensorsIncorrect bearingCorrect bearing


Hyperbolic location theor y


The hyperbola is the set of points at a con-stant range-difference ( # ) from two foci

Each sensor pair gives a hyperbola on whic hthe emitter lies

Location estimation is inter section of all hy-perbolas

Hyperbola ofconstantrange−differance

21PSfrag replacements

$&% $&'

SensorsLocation estimate

Hyperbola from (1,2)

Hyperbola from (1,3)

Hyperbola from (2,3)3

21PSfrag replacements


Location on Conic Axis (LOCA) solution


With three sensor s, rewrite hyperbolic equations to form mathematical dual .

Sensor s on a conic with emitter at a focus.

Foci ambiguity:– Conic is ellipse: the ‘wr ong’ focus gives negative time-dela ys.

– Conic is hyperbola: the ambiguity is unresolv able

SensorsLocation estimates

conic axis

foci


coni

c ax

is

foci

coni

c ax

is

conic axis

foci

foci


Hyperbola − unremovable ambiguityEllipse − removable ambiguity









conic axis

foci


coni

c ax

is

foci

coni

c ax

is

conic axis

foci

foci











conic axis

foci


coni

c ax

is

foci

coni

c ax

is

conic axis

foci

foci



TDOA-8-bPWB January 15, 2002

LOCA solution


With four or more sensor s, conic type irrele vant

Each triad of sensor s gives a line (conic axis) containing emitter

– Number of conic axes = Number of triads =(*) )

Least-squares solution finds the point closest to all the lines

Feasib le rang e-diff erence bivector correction as prefilter

SensorsLocation estimate


Clustered netw orks


Intra-c luster distance is much less than inter -cluster distance .

Compute TDOAs within cluster s to minimiz e comm unication rang e

Each cluster pub lishes results

Users and other cluster s subscribe with long distance comm unication andfuse results

Inter−cluster communication

Long range publish/subscribeUser/hub

SensorsClusters

Comm.

RemoteComm.

Local

On−node processing


Outline


Motiv ation



GPS averaging


Summar y


Cluster comm unication constraints


PSfrag replacements

x (meters)y

(met

ers)

Local communication

x (meters)

y(m

eter

s)

Global communication

x (meters)

y(m

eter

s)

x (meters)

y(m

eter

s)

x (meters)

y(m

eter

s)

Sch

mid

tLO

CA

Sph

er.

Inte

rp.

Bea

ring

Inte

rp.

0 500 1000

0 500 10000 500 1000

0 500 10000 500 1000

0

200

400

600

800

1000

0

100

200

0

200

400

600

800

1000

0

20

40

0

200

400

600

800

1000

0

100

200

0

200

400

600

800

1000

0

20

40

0

200

400

600

800

1000

0

100

200


Geometric precision sim ulations


PSfrag replacements

x (meters)

y(m

eter

s)+

rms ,

x (meters)

y(m

eter

s)

+rms ,

x (meters)

y(m

eter

s)

+rms ,

x (meters)

y(m

eter

s)

+rms ,

0 500 10000 500 1000

0 500 10000 500 1000

0

200

400

600

800

0

50

100

150

200

0

200

400

600

800

0

50

100

150

200

0

200

400

600

800

0

50

100

150

200

0

200

400

600

800

0

50

100

150

200


Outline


Motiv ation



GPS averaging


Summar y


SITEX02 Frequenc y Estimation


PSfrag replacements

Fre

q(H

z)

50

60

70

80

90

100

110

120

130

140

150


SITEX02 2-Node Delay Estimation


origin1

PSfrag replacements

Del

ay(s

)

Time (s)0 200 400 600 800 1000 1200 1400

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1


Outline


Motiv ation



GPS averaging


Summar y




Inter -node time sync hronization is essential for many algorithms– Localization error is related to percenta ge error in time delay estimates.

– Using nodes spaced at 100m, 2.9ms is 1% error

– Nodes might be spaced more closel y

Crystal oscillator s drift at 10-100 ppm, equiv alent to accum ulated error of36-360 ms per hour of operation.

External time reference is necessar y.

NTP is a free program for sync hronizing computer s over a netw ork

Also sync hroniz es cloc ks from reference signals (GPS, radio signal, etc)– GPS provides a timing signal accurate to 100ns.

– Not availab le on Sensoria hardware (firmware/software upgrades will correct this)

NTP netw ork sync hronization used for SITEX02. Accurac y often better than10ms, depending on netw ork load.


Single Node Time Skew


PSfrag replacements

UTC

Tim

esk

ew

(s)

SH4 Time

DSP Time

21:45 21:50 21:55 22:00 22:05 22:10 22:15-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06


Time Skew


PSfrag replacements

UTC

Tim

esk

ew

(s)

DSP Time

SH4 Time

21:45 21:50 21:55 22:00 22:05 22:10 22:15-0.15

-0.1

-0.05

0

0.05

0.1


Outline


Motiv ation



GPS averaging


Summar y


GPS averaging


Handheld GPS used at SITEX02for node sur veying

Single GPS measurement is ac-curate to 10m

GPS averaging over severalhour s significantl y impr oves theaccurac y.

PSfrag replacements

mm

-10 0 10-10

0

10

PSfrag replacements

Meters

Met

ers

-200 -100 0 100 200

-100

0

100

200

300


Outline


Motiv ation



GPS averaging


Summar y




5-element acoustic sensor

Coherent bearing estimation & classification

Provides additional ground truth for later analysis

PSfrag replacements

Nov 14 2001, UTC

Bea

ring

tota

rget

18:30 19:00 19:30 20:00 20:30-200

-100

0

100

200


Outline


Motiv ation



GPS averaging


Summar y– Contrib utions– Future work– Publications


Contrib utions


SITEX02: TDOA software ran in real-time over ISI Diffusion routing

TDOA perf ormance sim ulations- demonstrate benefits of cluster -basedprocessing

Enhanced SITEX02 recor ded data by implementing time sync h/GPSaccurac y impr ovements

Collected ground-truth bearing data

Demonstrated bearing fusion and used Declarative Routing Protocol in For tBenning Tanks Under Trees demonstration, Oct 2001.

Released/documented DRP software


Future work


TDOA enhancements– Impr ove frequenc y trac ker for high-band width data

– Extend to multi-tar get trac king

– Integrate with trac king/displa y system

Dynamic cluster formation

Acoustic multipath mitigation

On-line refinement of sensor locations and/or time sync hronization

NTP and Lin ux fix es to allo w GPS time sync hronization


Publications


P. W. Boettc her and G. A. Shaw, “A Distrib uted Time-Diff erence of Arriv alAlgorithm for Acoustic Bearing Estimation”, in Proc. 2001 InternationalConf . on Information Fusion, vol. 1, Montreal, August 2001.

P. W. Boettc her, J. A. Sherman, and G. A. Shaw, “Target localization usingacoustic time-diff erence of arriv al in distrib uted sensor netw orks”, to appearin SPIE AeroSense , Orlando, FL, April 2002.

Project repor ts– Declarative Routing Protocol project repor t

– TDOA sim ulation project repor t


Bearing example


PSfrag replacements

Local time (hh:mm)

Bea

ring

tota

rget

(deg

rees

)

08:12 08:14 08:16 08:18-200

-150

-100

-50

0

50

100

150


Documents

A Distributed Time-Difference of Arrival Algorithm for Acoustic … · 2018-01-10 · P. W. Boettcher, J. A. Sherman, and G. A. Shaw, “Target localization using acoustic time-difference