MIMO radar target tracking using the probability hypothesis density filter

Abstract

Target tracking in a widely spread multiple input multiple output (MIMO) radar system requires joint processing of several measurements from multiple sensors. The probability hypothesis density (PHD) filter provides a promising framework to process these measurements, since it does not require any measurement-to-track associations. Furthermore, the PHD filter naturally handles a multi-target environment because of the lack of explicit data association. We implement a PHD filter in the GTRI/ONR MIMO Benchmark, and compare results against the Benchmark's default solution. We assume a linear Gaussian target model so that the posterior target intensity at any time step is a Gaussian mixture (GM). Under this assumption, the PHD filter has closed-form recursions and target state extraction is simplified. This paper focuses on our implementation of the GM-PHD filter in the MIMO Benchmark, along with practical issues such as track labeling and applying the filter for the case of multiple sensors.