Tracking of Uncooperative Targets Using Reachability Analysis

This paper presents a nonlinear filter to track a maneuvering target while accounting for a priori information about its maneuvering capabilities. The idea is to compute the probabilistic reachable set of the target given a priori distributions for its initial condition and maneuvering capabilities between two measurement time intervals. A non-product quadrature scheme, the Conjugate Unscented Transformation (CUT), is used to compute the statistical moments of the target state accurately with a minimal number of samples. A minimum variance framework is used to assimilate sensor observation with model prediction to estimate the target state and corresponding state covariance. The performance of the derived filter is benchmarked against a conventional sigma-point filter by considering the problem of tracking an automobile on a road network.