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Decentralized predictive sensor allocation

Decentralized predictive sensor allocation. Mark Ebden , Mark Briers , and Stephen Roberts Pattern Analysis and Machine Learning Group Department of Engineering Science University of Oxford QinetiQ Ltd. Malvern Technology Centre United Kingdom. JDL MODEL. SENSOR MANAGER. *. *.

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Decentralized predictive sensor allocation

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  1. Decentralized predictive sensor allocation Mark Ebden, Mark Briers, and Stephen Roberts Pattern Analysis and Machine Learning Group Department of Engineering Science University of Oxford QinetiQ Ltd. Malvern Technology Centre United Kingdom

  2. JDL MODEL SENSOR MANAGER * *

  3. Motivation

  4. Motivation

  5. Motivation OPTION 1

  6. Motivation OPTION 1 OPTION 2

  7. 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

  8. 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

  9. Forecasting Present tW t1 t2 • 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 s1 s2 s3

  10. The DCF principle Present tW t1 t2 s1 s2 s3

  11. The DCF principle Present tW t1 t2 s1 s2 s3

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

  13. The simulation • A simulated sensor network was applied to see how well the DCF algorithm copes with real data Sensor Network IdentificationPerformance Target trails DCF Algorithm

  14. Results: CF vs DCF

  15. Conclusions • Decentralized response to dynamic environmentsmessage passingDCF principle • Future work: • QinetiQ are currently pursuing exploitation • Oxford are generalizing the algorithm to handle other scenarios, such as RoboCup Rescue

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

  17. ▪EXTRA SLIDES ▪

  18. Sensor arrangement • Assume targetsidentifiable at<120 mph • Assume pivoting180° requires 10 s • Assume zoomingand focusing by 180° requires 3 s

  19. Increasing the challenge • DCF is useful when targets require simultaneous tracking: here, 5 targets at a time, over 3 minutes 5 targets at a time Targets with 4+ data points

  20. 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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