PHD filtering in presence of highly structured sea clutter process and tracks with extent

Multitarget tracking is fundamental in many security and surveillance applications. However, as algorithms have been applied in more and more challenging environments, traditional simplifications no longer even approximately hold true. In particular, we consider the problem of maritime surveillance in which small targets (boats) are to be detected and tracked in the presence of highly structured noise (waves). In particular, we consider two problems. The first is that, given the resolution of modern radar systems, even small targets are extended, straddling multiple range or azimuth bins. The second is that sea clutter is not a uniform, Poisson-distributed noise process but is highly spatially varying. In this paper, we develop two extensions of the Probability Hypothesis Density (PHD) Filter. Using a generalised likelihood model, extended targets can be readily accounted for. Through the use of spatially varying clutter models, structured noise approximation is provided. The algorithms were developed and tested using a sea trial in which Rigid-Hulled Inflatable Boats (RHIBs), equipped with GPS receivers, were tracked using a radar system.