Track-Before-Detect Using Histogram PMHT and Dynamic Programming

The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric mixture-fitting approach to the Track-before-detect (TkBD) problem. It has been shown to give performance close to numerical approximations of the optimal Bayesian filter at a fraction of the computation cost. This paper will consider an implementation of the H-PMHT for non-linear non-Gaussian TkBD problems using a dynamic programming fixed-grid approximation through application of the Viterbi algorithm. This alternate H-PMHT implementation is compared with Kalman Filter and Particle Filter H-PMHT implementations via simulated single target scenarios.