Mitigation of Wind Turbine Clutter With Digital Phased Array Radar

Abstract

Wind turbine clutter (WTC) contamination affects polarimetric meteorological observations and automatic high-impact weather detection algorithms in several ways, such as, misidentification of precipitation echoes, false mesocyclone detections, and incorrect storm cell identification. With the dramatic growth of the wind power industry, this would only aggravate in the future. Since fully digital Phased Array Radar (PAR) is a promising candidate technology for the next generation of weather radars, evolutionary signal processing algorithms that make use of their capabilities should be investigated to mitigate WTC contamination. This article investigates the use of Space-Time Adaptive Processing (STAP) to mitigate WTC contamination with ground-based polarimetric PAR. First, a flexible wind turbine time-series signal simulator is developed to characterize the contamination signatures in the space-time domain. Then, a STAP algorithm that removes the contamination is presented and demonstrated on simulated data. Two digital radar back-end architectures are considered to evaluate the performance of the proposed algorithm. One with digital sub-array outputs the other one with fully digital outputs (i.e., element-level digital). Results indicate the WTC spectrum has a characteristic structure in the space-time domain, and that biases induced in polarimetric weather variables can be more effectively mitigated using an fully digital PAR.