Moving target acquisition through state uncertainty minimization

This work addresses the task of a mobile sensor platform searching for a moving target. We show that minimizing the entropy of the probability distribution of the target state estimate can result in a control input for the mobile sensor that acquires the target in less iterations than an exhaustive search. We also show that this approach can be used to track the target after it is acquired. We apply a particle filter framework to estimate the state of the target and propose an information-based cost function to optimize as part of a control law for the mobile sensor. We include simulation results to illustrate the performance of our approach.