Distinguishing Wanted and Unwanted Targets Using Point Processes

In many applications, objects of interest navigate in the same environment with unimportant objects that show similar motion behaviours. One prominent example is maritime surveillance in the presence of sea clutter since the sea often looks like a strongly fluctuating population of real targets due to the temporal correlation found in radar measurements of the sea surface. Conventional clutter models usually do not account for temporal correlation but model clutter as spontaneous instances of false measurements. In contrast, it would be desirable to describe such ""undesired targets"" with their own mathematical model in order to distinguish them properly from the population of true targets. This paper presents a variation of the Panjer Probability Hypothesis Density (PHD) filter which propagates two populations at the same time, assuming their independence. The performance of the proposed method is analysed on simulated data using a Gaussian-Mixture implementation.