Multiple targets tracking by optimized particle filter based on multi-scan JPDA

In this paper, the particle filter is used to solve the nonlinear and nonGaussian estimation problem in multiple targets tracking and multiple sensor fusion process. The weight of the particle is evaluated through the combination of Joint Probability Data Association (JPDA) and multiple hypothesis tracking (MHT), which makes the probabilistic assignment based on all reasonable hypotheses in a sliding window of multiple scans. To track the multiple targets with random varying velocities, each particle's state is optimized based on the history information from the previous scans in the sliding window and group information in the current scan. The particle diversity is enriched while the trajectory of each particle evolves towards the high posterior density distribution. Moreover the problem of tracking newly appeared objects or disappeared objects are also discussed. The simulation results show that the improved particle filter method achieves dynamic stability and robustness while tracking multiple random moving targets.

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