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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
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JDL MODEL
SENSOR MANAGER
**
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Motivation
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Motivation
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Motivation
OPTION 1
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Motivation
OPTION 1
OPTION 2
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– 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
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– 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
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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
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The DCF principle
Present t1 t2 tW
s1
s2
s3
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The DCF principle
Present t1 t2 tW
s1
s2
s3
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The database
• Outdoor area observed with one sensor for one hour• 80 of the 522 targets have more than one data point
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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
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Results: CF vs DCF
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• 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
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Thank you
Members of the ARGUS II project: (www.argusiiproject.org)
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▪ EXTRA SLIDES ▪
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Sensor arrangement
• Assume targetsidentifiable at<120 mph
• Assume pivoting180° requires 10 s
• Assume zoomingand focusing by 180° requires 3 s
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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
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1 2 3 4 5 6 7 8 9 1010
-1
100
101
102
Number of sensors
CP
U t
ime
per
sens
or (
seco
nds)
1
2
3
4
5
6
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