Decentralized predictive sensor allocation

Mark Ebden, Mark Briers, and Stephen Roberts

Pattern Analysis and Machine Learning GroupDepartment of Engineering Science

University of Oxford

QinetiQ Ltd.Malvern Technology Centre

United Kingdom

JDL MODEL

SENSOR MANAGER

**

Motivation

Motivation

Motivation

OPTION 1

Motivation

OPTION 1

OPTION 2

– Each sensor has a neighbourhood – itself plus all the sensors which can observe the same targets as it can

– Before evaluating a possible coalition switch, the sensor receives a report from each of its neighbours on the expected ramifications in the neighbours’ neighbourhoods

– Although there is significant redundancy (overlap among the reports), this decentralization avoids “combinatorial explosion” in large sensor networks

Message passing for coalition formation

– Each sensor has a neighbourhood – itself plus all the sensors which can observe the same targets as it can

– Before evaluating a possible coalition switch, the sensor receives a report from each of its neighbours on the expected ramifications in the neighbours’ neighbourhoods

– Although there is significant redundancy (overlap among the reports), this decentralization avoids “combinatorial explosion” in large sensor networks

Message passing for coalition formation

Forecasting

Present t1 t2 tW

s1

s2

s3

• Might consider one time step ahead. For time t1, assess the projected value of changes to each sensor’s orientation and field of view

• Myopic unless sensors can adjust very quickly

The DCF principle

Present t1 t2 tW

s1

s2

s3

The DCF principle

Present t1 t2 tW

s1

s2

s3

The database

• Outdoor area observed with one sensor for one hour• 80 of the 522 targets have more than one data point

The simulation

• A simulated sensor network was applied to see how well the DCF algorithm copes with real data

Target trails

Sensor Network

DCF Algorithm

IdentificationPerformance

Results: CF vs DCF

• Decentralized response to dynamic environments

message passing DCF principle

• Future work:– QinetiQ are currently pursuing exploitation– Oxford are generalizing the algorithm to handle other

scenarios, such as RoboCup Rescue

Conclusions

Thank you

Members of the ARGUS II project: (www.argusiiproject.org)

▪ EXTRA SLIDES ▪

Sensor arrangement

• Assume targetsidentifiable at<120 mph

• Assume pivoting180° requires 10 s

• Assume zoomingand focusing by 180° requires 3 s

Increasing the challenge

• DCF is useful when targets require simultaneous tracking: here, 5 targets at a time, over 3 minutes

Targets with 4+ data points 5 targets at a time

1 2 3 4 5 6 7 8 9 1010

-1

100

101

102

Number of sensors

CP

U ti

me

per s

enso

r (se

cond

s)

1

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5

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Speed comparison with centralised algorithm:Artificial linear databases

– Each sensor can view three targets, one or (usually) two of which fall within range of other sensors