Space-Time Adaptive Processing Clutter Suppression Algorithm Based on Spatiotemporal Stationarity of Ionospheric Clutter in HFSWR

In high-frequency surface wave radar (HFSWR) systems, ionospheric clutter is a common phenomenon that often causes targets to be submerged. Space-time adaptive processing (STAP) is a classical adaptive filtering algorithm. To reduce computational complexity and the required number of clutter samples, practical radar systems usually employ reduced-dimension STAP techniques, such as the space-time multiple beam (STMB) algorithm. However, the classical STMB was originally designed to suppress ground clutter in airborne radar and is not directly applicable to ionospheric clutter in HFSWR. There are two main challenges when applying STMB to HFSWR systems. First, STMB selects auxiliary beams in a cross-shaped region around the target, but HFSWR suffers from extremely poor spatial mainlobe resolution of ionospheric clutter, which can cause the auxiliary beams to fall within the target’s mainlobe, leading to target cancellation. Second, STMB is suitable for stationary clutter, such as ground clutter, but not for nonstationary ionospheric clutter. To address these two challenges, this paper proposes the sidelobe space multiple beam (SB SMB) algorithm based on the space-time stationarity of ionospheric clutter. Experimental data show that the proposed algorithm achieves excellent suppression of patchy ionospheric clutter. This paper also explores the relationship between the size of the reduced-dimension region and algorithm performance.