Space-time adaptive processing algorithm based on hyper beamforming for ionospheric clutter suppression in small-array high-frequency surface wave radar

Small-array high-frequency surface wave radar (HFSWR) is widely used to monitor maritime targets as it can be used to save on-land resources. In small-array HFSWR systems, the main lobe of the receiving angle spectrum is significantly broadened. In complex clutter backgrounds, an extremely wide beam severely influences clutter suppression performance; consequently, targets with a low signal-to-clutter ratio (SCR) may be eliminated, or the angle may be barely estimated. This study proposes a space-time adaptive processing (STAP) algorithm based on hyper beamforming (HBF) to improve the clutter suppression performance of small-array HFSWR. In addition, HBF can obtain more independent identical distributed training samples than the conventional beamforming; thus, the STAP algorithm can extract the clutter information with high accuracy in the covariance matrix estimation. Moreover, this study combines an efficient STAP algorithm with a joint domain localised (JDL) algorithm to improve clutter suppression. Based on the experimental results, the proposed HBF-JDL algorithm performs satisfactorily and significantly improves the SCR. Moreover, HBF-JDL is still applicable at lower SCRs of the target compared with JDL.