Sea Clutter Suppression by Atomic Norm Minimisation in Frequency Diverse Array‐Space‐Time Adaptive Processing Radar Under Range Ambiguity

ABSTRACT Sea clutter suppression is a hot topic for airborne radar. Space‐time adaptive processing (STAP) is a useful approach to address this issue. Currently, range ambiguity is a problem to restrict conventional STAP performance. The conventional STAP cannot differentiate the signals originating from distinct ambiguous areas due to the lack of range‐associated degrees‐of‐freedom (DoFs). Frequency diverse array (FDA) can provide the DoFs via introducing a frequency shifting between adjacent transmit elements, then FDA‐STAP is developed. According to the Reed, Mallet and Brennan (RMB) rule, FDA‐STAP requires more training samples in comparison with conventional STAP. However, the number of training samples is limited practically, and FDA‐STAP will suffer from severe performance deterioration. To address this issue, this paper introduces a sparsity recovery algorithm, atomic norm minimisation (ANM), into FDA‐STAP for clutter profile recovery, that is, ANM‐FDA‐STAP, thereby reducing the requirement on training samples. Numerical results verify that the ANM‐FDA‐STAP algorithm exhibit outstanding performance.