A new histogram PMHT incorporating pixel noise distribution for dim target tracking

The histogram probabilistic multi-hypothesis tracker (H-PMHT) is an attractive multi-target tracking method which directly processes raw sensor images to detect dim targets. In the H-PMHT, the raw sensor images are converted to histograms, and then the histograms are assumed to follow the multinomial distributions parameterized by mixture density functions, in which each mixture component corresponds to a target object or clutter. Combine this measurement model with the expectation-maximization (EM) method, H-PMHT estimates the states of targets and the mixture proportions. Recently, by assuming alternative measurement models based on Poisson distribution and Interpolated Poisson distribution, researchers proposed the Poisson H-PMHT (P-HPMHT) and the Interpolated Poisson PMHT (IP-PMHT) to allow for fluctuating target amplitude. However, these methods fail to take distribution information of pixel noise into tracking consideration, which then results the degradation of detection performance. In this paper, we address this problem by modifying the measurement model of IP-PMHT to allow for incorporating statistical information of pixel noise. A key point to achieve this is that Interpolated Poisson follows a thinning property, which means that the energy from clutter can be modeled with a parameterized Interpolated Poisson in the IP-PMHT. We replace the parameterized Interpolated Poisson with a given distribution, which describes the pixel noise, and propose a new tracking method. An important feature of this new method is that it retains the advantages of the H-PMHT, meanwhile naturally incorporates the prior information about pixel noise in target tracking. Through the Mont Carlo simulations, we prove the superiority of this new method in dim target tracking.