Exploiting All Digital Phased Array Radars for Clutter Mitigation with Space-Time Adaptive Processing

Weather radar data quality can be improved by the mitigation of clutter contamination. One common source of contamination is ground clutter, which has a narrow Doppler spectrum and a mean velocity of approximately 0 ms^-1. Clutter from stationary ground targets can successfully be mitigated with a spectral filter, whereby a notch is placed near the center of the spectrum to remove this non-moving clutter. In contrast, moving clutter can have non-zero Doppler velocity and potentially a wider spectrum width. It can be a challenge to separate moving clutter from weather with conventional clutter filtering algorithms.

Digital phased array radar (PAR) can sample received signals at multiple phase centers, which enables digital beamforming (DBF) and can provide additional information in the spatial domain. Space-time adaptive processing (STAP) consists of designing a two-dimensional space-time filter to suppress contaminating signals through the joint use of DBF and spectral Doppler processing. This can be exploited to enhance clutter mitigation performance. We present the application of STAP to mitigate various types of clutter (e.g., airborne, cars, or wind turbines). The benefits of STAP for non-stationary clutter are shown through simulations and statistical analysis. The performance of adaptive processing is evaluated by comparing it with non-adaptive space-time processing by applying the two-dimensional Fourier transform. Finally, tradeoffs in clutter mitigation performance are studied as a function of PAR back-end architecture, comparing fully digital and subarray-based systems.