A real-time multiple target tracking algorithm using merged probabilistic data association technique and smoothing particle filter

摘要

In this paper, we present a tracking system that combines the merged probabilistic data association (MPDA) technique together with the smoothing particle filter to track multiple targets. The MPDA approach combines the probabilistic nearest-neighbor filter (PNNF) together with the probabilistic data association (PDA) approach, in the data association step, to track multiple targets in dense clutter environment. Due to the high uncertainty when applying a particle filter to track a maneuverable target, the smoothing particle filter is used. Results show that combining MPDA together with smoothing particle filter can achieve a robust and real-time tracking system for tracking multiple targets even in dense clutter environment.