A Clutter Mitigation Technique for Phased Array Weather Radar

Weather radar data are often contaminated by unwanted clutter from stationary and/or moving objects, complicating the accurate interpretation of weather phenomena. Traditional clutter mitigation techniques such as the Gaussian Model Adaptive Processing (GMAP) are effective against stationary clutter but struggle with moving clutter due to the variable, non-zero Doppler velocities. Phased Array Radars (PARs), with capabilities such as Digital Beamforming (DBF), offer the possibility for advanced clutter classification and mitigation by leveraging both spatial and spectral information. This paper proposes an adaptive clutter mitigation algorithm for weather radars. Initially, the algorithm employs Fourier DBF to classify clutter signals. Next, adaptive DBF is applied to the transmit beam where clutter was identified in the previous step. During this stage, artificial high power due to sidelobes and grating lobes is filtered out. Subsequently, adaptive spectral analysis is performed to filter out the clutter through the power spectrum density. This algorithm optimizes data quality while minimizing computational load by selectively performing adaptive processing only when necessary. The algorithm's effectiveness is evaluated using data from the Horus radar system, demonstrating its capability to accurately classify clutter and weather, improving Doppler velocity estimation and overall weather data quality by filtering out clutter.