Radiation intensity Gaussian mixture PHD filter for close target tracking

Abstract

The Gaussian mixture probability hypothesis density (GM-PHD) filter has recently been devised as a suboptimal Bayesian solution for multiple target tracking. However, the performance of standard GM-PHD degrades seriously in close targets tracking. Although the renormalization scheme is proposed to improve the estimation results, the identification problem of measurements generated by different targets and clutter is still unresolved. To this end, this paper develops a radiation intensity PHD filter (RIGM-PHD). First, the Gaussian distribution with signal-to-noise (SNR) information is proposed to model the radiation intensity and describe the relationship between the target radiation intensity and clutter level. Then, in order to circumvent the issue that the real target SNR can't be obtained precisely, we construct a likelihood function of radiation intensity for unknown target SNR and derive new PHD recursion equations. Second, a labeling update scheme is also provided to prevent the incorrect propagation of identities of close targets. Finally, comprehensive Monte Carlo simulations in terms of various detection probabilities and clutter rates for parallel and crossing targets are performed to investigate the effectiveness of the proposed filter.