Penalized Gaussian mixture probability hypothesis density filter for multiple target tracking

Bayesian multi-target filter develops a theoretical framework for estimating the full multi-target posterior which is intractable in practice. The probability hypothesis density (PHD) is a practical solution for Bayesian multi-target filter which propagates the first order moment of the multi-target posterior instead of the full version. Recently, the Gaussian Mixture PHD (GM-PHD) has been proposed as an implementation of the PHD filter which provides a close form solution. The performance of this filter degrades when targets are moving near each other such as crossing targets. In this paper, we propose a novel approach called penalized GM-PHD (PGM-PHD) filter to improve this drawback. The simulation results provided for various probabilities of detection, clutter rates, targets velocities and frame rates indicate that the proposed method achieves better performance compared to the GM-PHD filter.

► We propose a penalization approach called PGM-PHD filter to improve this drawback of GM-PHD filter.
► The performance of the filters are compared using various probabilities of detection and clutter rates.
► The effect of different targets velocities and data rates are also studied.
► The simulation results show that our method improves the performance of GM-PHD filter for tracking closely space targets.