Mitigation of Ground-Clutter Effects by Digital Beamforming With Precomputed Weighting Matrix for Phased Array Weather Radar

Mitigating ground-clutter signals is one of the major topics for the development of phased array weather radars (PAWRs). Adaptive digital beamforming (DBF) methods have been proposed to reduce ground-clutter effects by making null beam patterns toward the ground. On the other hand, adaptive DBF methods require much higher computational costs than the nonadaptive Fourier (FR) DBF method. In the present study, we propose the “precomputed method” for weather radar applications. In this method, a weighting matrix of DBF is once calculated by an adaptive DBF method with observation data recorded in fair weather, and is applied to real-time processing. Since the weighting matrix is not updated in real time, the computational cost becomes 30 times less than the adaptive DBF, the same level as the FR method. At azimuth angles where ground clutter is modest, the results of the FR method may be better than those of the proposed method. In those cases, the FR method can be used selectively for less ground-clutter regions. This study demonstrates a practical method to reduce the ground-clutter effect of PAWRs by DBF.