Integrated clutter estimation and target tracking using Poisson point process

Abstract

In this paper, methods of tracking multiple targets in non-homogeneous clutter background is studied. In many scenarios, after detection process, measurement points provided by the sensor (e.g., sonar, infrared sensor, radar) are not distributed uniformly in the surveillance region. On the other hand, in order to obtain accurate results, the target tracking filter requires information about clutter's spatial density. Thus, non-homogeneous clutter point spatial density has to be estimated based on the measurement point set and tracking filter's outputs. Also, due to the requirement of compatibility, it is desirable for this estimation method to be integrated into current tracking filters. In this paper, a recursive maximum likelihood method and an approximated Bayesian method are proposed to estimate the clutter point spatial density in non-homogeneous clutter background and both will in turn be integrated into Probability Hypothesis Density (PHD) filter. Here, non-homogeneous Poisson point processes, whose intensity function are assumed to be mixtures of Gaussian functions, are used to model clutter points. The mean and covariance of each Gaussian function is estimated and used in the update equation of the PHD filter. Simulation results show that the proposed methods are able to estimate the clutter point spatial density and improve the performance of PHD filter over non-homogeneous clutter background.