Probability hypothesis density filtering with multipath-to-measurement association for urban tracking
Meng Zhou, Jun Jason Zhang, Antonia Papandreou‐Suppappola · 2012
We consider the particle probability hypothesis density filter (PPHDF) for tracking multiple targets in urban terrain. This is a filtering technique based on random finite sets, implemented using the particle filter. Unlike data association methods, the PPHDF can be modified to estimate both the number of targets and their corresponding tracking parameters. We propose a modified PPHDF algorithm that employs multipath-to-measurement association (PPHDF-MMA) to automatically and adaptively estimate the available types of measurements. By using the best matched measurement at each time step, the new algorithm results in improved radar coverage and scene visibility. Numerical simulations demonstrate the effectiveness of the PPHDF-MMA in improving the tracking performance of multiple targets and targets in clutter.