Adaptive processing in High Frequency Surface Wave Radar

This paper investigates the use of space-time adaptive processing (STAP) for high frequency surface wave radar (HFSWR) systems. STAP has hitherto been investigated in great detail for airborne radar systems; the majority of the adaptive algorithms that have been developed have been designed with a bias towards such applications. HFSWR systems are characterized by the severely limited number of data samples available to train the adaptive filter. In this paper we report on investigations in applying low-complexity STAP algorithms to HFSWR systems. In particular we focus on applying such algorithms on HFSWR data cube measurements heavily corrupted by ionospheric clutter. We then reflect on the implications of the results.