Distinguishing Small Targets from Sea Clutter Using Dynamic Models

Radar signal processing for maritime surveillance and reconnaissance is a difficult problem due to the occurrence of sea clutter. The sea surface is highly reflective and agile, therefore the received radar return has a high intensity and it is often correlated in time and space due to undulation. Classical approaches try to suppress the clutter signal on the sensor or measurement level, however such methods usually do not take temporal information over multiple dwells into account. This article provides an analysis of an approach that simultaneously tracks the observed objects and the correlated clutter signals and classifies them based on the correspondence of their state history with the target- and clutter-specific dynamic models. Furthermore, it is demonstrated that this tracking-based classifier maintains good accuracy even on downsampled data, which is representative of a scanning radar. The described method is tested on datasets of the 2006 Fynmeet trial conducted by the Council for Scientific and Industrial Research (CSIR).