Tracking Small Targets in Heavy-Tailed Clutter Using Amplitude Information

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. Target tracking in heavy-tailed environments is challenging even for moderate signal-to-noise ratios (SNRs) due to the increased frequency of target-like outliers. A tracking method operating in such an environment should exploit as much of the data as possible to ensure robustness. Still, conventional tracking methods rely on kinematic measurements such as range, bearing, and Doppler only. The performance of the tracking method can be improved by using the backscattered signal strengths together with the kinematic measurements. This is done in the probabilistic data association filter with amplitude information (PDAFAI). We propose new conservative amplitude likelihoods for the PDAFAI with improved robustness compared to existing methods. The first likelihood works by incorporating the uncertainty of the background estimate. The second likelihood explicitly treats the background as heavy tailed using the K-distribution. Extensive and realistic Monte Carlo simulations show that both our conservative likelihoods give significant reductions in track loss. Furthermore, we provide a quantitative evaluation of the difficulties encountered by tracking methods in heavy-tailed clutter. To the best of our knowledge, such an analysis does not yet exist in the open literature.