Adaptive Nullforming to Mitigate Ground Clutter on the National Weather Radar Testbed Phased Array Radar

With the decreasing cost of phased array antennas, their use for weather surveillance is becoming more practical. A significant advantage of phased arrays that can be applied to weather surveillance is spatial filtering. Using adaptive nullforming to spatially filter clutter is a novel approach to clutter mitigation, which is not possible with conventional parabolic reflector antennas. Moreover, spatial filtering is also applicable to phased-array-specific techniques such as beam multiplexing and adaptive scanning when only a few pulses are available for processing; this situation is particularly challenging for conventional ground clutter filters. The National Weather Radar Testbed Phased Array Radar (NWRT PAR) provides an opportunity to test some of these new capabilities. In this paper, a linearly constrained minimum power algorithm with an additional quadratic constraint is applied to weather data collected using the NWRT PAR and its multichannel receiver. Both the original algorithm and a recursive least squares version are utilized to show reflectivity and velocity data where both weather and ground clutter are present. Doppler spectra from selected range gates are examined to illustrate the performance of adaptive nullforming. Issues such as the number of samples needed to estimate the covariance matrix are explored. As far as we know, this is the first time that these types of techniques have been used to mitigate ground clutter contamination on a weather surveillance radar.