Integrated Local Linearization Particle Filter for Multiple Maneuvering Target Tracking in Clutter

Abstract

The integrated particle filter (IPF) algorithm is proposed for single target tracking in clutter that combines the existing particle filters with false track discrimination (FTD) which distinguishes between the true tracks and the false tracks using the target existence probability as a track quality measure. To improve the tracking performance of IPF for maneuvering multitarget tracking, we propose an integrated local linearization particle filter (ILLPF) that applies the FTD to LLPF which approximates the optimal importance density with the updated estimates of a bank of tracking filters. The proposed algorithm is extended to accommodate interacting multiple model-linear multitarget-ILLPF (IMM-LM-ILLPF) for maneuvering target tracking with multiple target dynamic models for robust tracking. A study with Monte Carlo simulation demonstrates the improvement of maneuvering multitarget tracking performance in cluttered environments.