Online clutter estimation using a Gaussian kernel density estimator for target tracking

In this paper, based on non-homogeneous Poisson point processes (NHPP), a kernel clutter spatial intensity estimation method is proposed. Here, the clutter spatial intensity estimation problem is decomposed into two parts: (1) estimate the probability distribution of the clutter number per scan; (2) estimate the spatial variation of the clutter intensity in the measurement space. Under the NHPP assumption, the empirical mean is used to get a maximum likelihood estimate for the first problem. For the second problem, an online locally adaptive Gaussian kernel density estimator is proposed. In addition, the proposed clutter estimation method is integrated with standard multitarget trackers, like Multiple Hypothesis Tracker (MHT), Joint Integrated Probabilistic Data Association (JIPDA) tracker, Probability Hypothesis Density (PHD) filter. Simulation results show that the proposed clutter spatial intensity estimator can improve the performance of the multitarget tracker in the presence of non-homogeneous clutter background.