Clutter Reduction for Phased-Array Weather Radar Using Diagonal Capon Beamforming With Neural Networks

Abstract

The X-band phased-array weather radar (PAWR) operated by the Osaka University, performs a full-volume scan every 30 s within a 60-km range. For the waves received by the array antenna of the PAWR, digital beamforming is used only in the elevation angles. The sidelobes of the beam pattern cause errors in spectral moments in the higher elevation angles because of ground clutter. For clutter reduction, a Capon beamformer techniques with diagonal loading (CPDL) with a neural network (NN) is applied to the PAWR. In comparison with the Fourier transform beamforming method, the effectiveness of the CPDL with NN method for ground clutter reduction is discussed, using numerical simulation, and actual PAWR measurement data. Based on the simulation results and measured data, we established that the CPDL with NN accurately estimates point and distributed scatterers, which simulate ground clutter and precipitation, respectively.