On maneuvering target tracking via the PMHT

Abstract

This paper presents an iterative off-line optimal state estimation algorithm, which yields the maximum a posteriori (MAP) state trajectory estimate of the state sequence of a target maneuvering in clutter. The problem is formulated as a jump Markov linear system and the expectation maximization algorithm is used to compute the state sequence estimate. The proposed algorithm optimally combines a hidden Markov model and a Kalman smoother to yield the MAP target state sequence estimate. The algorithm proposed uses probabilistic multi-hypothesis tracking (PMHT) techniques for tracking a single maneuvering target in clutter. Previous applications of the PMHT technique have addressed the problem of tracking multiple non-maneuvering targets. These techniques are extended to address the problem of optimal (in a MAP sense) tracking of a maneuvering target in clutter.