Performance of PDAF-based tracking methods in heavy-tailed clutter

Harbor surveillance above and below the sea surface depends on sensors such as surveillance radar and multibeam sonar. These sensors attempt to detect and track moderately observable targets such as small boats or human divers in environments which often are characterized by heavy-tailed backgrounds. This paper provide simulation results which quantify the inevitable performance loss encountered in heavy-tailed environments. The results show that the performance loss can be reduced by accounting for heavy-tailedness in the detection and tracking processes, and by the utilization of Amplitude Information (AI). Two new amplitude likelihoods developed in a preceding paper come favorably out of this comparison. Furthermore, the evaluation of the Modified Riccati Equation (MRE) is outlined for the combination of AI and heavy-tailed clutter. The MRE can be used to decide the false alarm rate for the detection process preceding target tracking.