Approximate dynamic programming for sensor management

This paper studies the problem of dynamic scheduling of multi-mode sensor resources for the problem of classification of multiple unknown objects. Because of the uncertain nature of the object types, the problem is formulated as a partially observed Markov decision problem with a large state space. The paper describes a hierarchical algorithm approach for efficient solution of sensor scheduling problems with large numbers of objects, based on a combination of stochastic dynamic programming and nondifferentiable optimization techniques. The algorithm is illustrated with an application involving classification of 10,000 unknown objects.