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Motivation
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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
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
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
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