|Stochastic Systems Group|
Sensor management for multiple target tracking with heterogeneous sensor models
Modern sensors are able to rapidly change mode of operation and steer between physically separated objects. While control of such sensors over a rolling planning horizon can be formulated as a dynamic program, the optimal solution is inevitably intractable. In this paper, we consider the control problem under a restricted family of policies and show that the essential sensor control trade-offs are still captured. The advantage of this approach is that one can obtain the optimal policy within the restricted class in a tractable fashion, in this case by using the auction algorithm. The approach is well-suited for problems in which a single sensor (or group of sensors) is being used to track many targets using a heterogeneous sensor model, i.e., where the quality of observations varies with object state, such as due to obscuration. Our algorithm efficiently weighs the rewards achievable by observing each target at each time to find the best sensor plan within the restricted set. We extend this approach using a roll-out algorithm, to handle additional cases such as when observations take different amounts of time to complete.
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