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 implementationofthe GM-PHDfilterinthe MIMO Benchmark, along with practical issues such as track labeling and applying the filter for the case of multiple sensors.
MIMO radar target tracking using the probability hypothesis density filter
2012-03-01
597207 byte
Conference paper
Electronic Resource
English
Multiple-Model Probability Hypothesis Density Filter for Tracking Maneuvering Targets
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