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 * *

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 t1t1 t2t2 tWtW s1s1 s2s2 s3s3 Might consider one time step ahead. For time t 1, 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 t1t1 t2t2 tWtW s1s1 s2s2 s3s3

The DCF principle Present t1t1 t2t2 tWtW s1s1 s2s2 s3s3

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 Identification Performance

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: (

▪ EXTRA SLIDES ▪

Sensor arrangement Assume targets identifiable at <120 mph Assume pivoting 180° requires 10 s Assume zooming and 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

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