Main-lobe clutter suppression algorithm based on rotating beam method and optimal sample selection for small-aperture HFSWR

When the azimuths of clutter and targets are in the same beam bin, the clutter is called main-lobe clutter of the corresponding targets. In small-aperture high-frequency surface wave radar (HFSWR) systems, the angle spectrum of main-lobe clutter suffers from severe broadening under the influence of the smaller array aperture, which can affect the detection performance of moving vessels. This situation occurs because the target vessels are more easily submerged in this broadened angle spectrum and can hardly be detected. In this study, a novel two-dimensional cascaded algorithm for main-lobe clutter that is based on combining an adaptive selection strategy for the optimal training samples and the rotating spatial beam method is proposed to suppress the clutter in both the angle domain and range domain. First, the correlation between training samples is analysed with the angle-Doppler joint eigenvector method. Then, the samples that are similar to the cell under test serve as training samples. Finally, the secondary beams that have Euclidean distances closest to the main beam are chosen. The experimental results of simulation and measured data confirm that the proposed approach provides far superior suppression performance and has strong robustness against array amplitude-phase errors and beam deviation.