Efficient methods of non-myopic sensor management for multitarget tracking

This paper develops two efficient methods of long-term sensor management and investigates the benefit in the setting of multitarget tracking. The underlying tracking methodology is based on recursive estimation of a joint multitarget probability density (JMPD), implemented via particle filtering methods. The myopic sensor management scheme is based on maximizing the expected Renyi divergence between the JMPD and the JMPD after a new measurement is made. Since a full non-myopic solution is computationally intractable when looking more than a small number of time steps ahead, two approximate strategies are investigated. First, we develop an information-directed search which focuses Monte Carlo evaluations on action sequences that are most informative. Second, we give an approximate method of solving Bellman's equation which replaces the value-to-go with an easily computed function that approximates the long term value of the action. The performance of these methods is compared in terms of tracking performance and computational requirements.