Extended Target Tracking With Multipath Detections, Terrain-Constrained Motion Model and Clutter

Abstract

To address the problem of extended target tracking (ETT) with measurement-origin uncertainty in a multipath environment with terrain-constrained motion model, a new generalized version of the standard probabilistic data association (PDA) filter, called MP-ET-PDA, based on random matrices (RM) is proposed in this paper. In the MP-ET-PDA filter, we assume that multipath detections and clutter are possible in the extended target tracking problem, which are prevalent in practical systems but barely addressed in the literature. Further, a clustering-aided MP-ET-PDA algorithm with a reduced computational complexity that makes use of the Variational Bayesian (VB) technique, called MP-ET-PDA-VB, is presented to provide near real-time processing capability in ETT problems with an uncertain multipath environment. In addition to using a constant velocity motion model, a new terrain-constrained motion model is presented for scenarios where terrain-following is required by motion-constrained autonomous vehicles. The posterior Cramér-Rao lower bound (PCRLB), which quantifies the best possible accuracy in realistic ETT problems with multipath detections and measurement-origin uncertainty, is derived as the benchmark for performance evaluation. Simulations results demonstrate the improved performance of the proposed algorithms.