A comparison of information theoretic functions for tracking maneuvering targets

Several information theoretic functions have been proposed in the literature to assess the information value of sensor measurements a posteriori, that is, after measurements have been obtained from one or more targets. Sensor planning algorithms, however, require that the value of future sensor measurements be computed a priori, based on available models and prior information. An approach was recently presented by the authors for estimating the expected information value of future sensor measurements in target classification problems. The approach derives expected information theoretic functions from probabilistic models of the sensors and the targets, conditioned on prior information. In this paper, the approach is extended to the problem of sensor planning for tracking maneuvering targets. The approach is illustrated for a sensor that obeys an exponential power law model of received isotropic energy, and a target that obeys a Markov motion model. The performance of five information theoretic functions is compared through numerical simulations, and the results show that the objective function based on conditional mutual information leads to the most effective sensor planning strategy.