A signal sub-space based approach for mitigating wind turbine clutter in fast scanning weather radar

Abstract

Removing turbine clutter from weather radar observations has become an essential problem in the community since wind turbine clutter signals (WTC) cannot be filtered using traditional clutter filtering. This paper addresses the problem of mitigation of WTC using knowledge of local precipitation and WTC signals to retain the maximum amount of precipitation and retrieve the filtered radar IQ data. The proposed algorithm uses the Generalized Likelihood Ratio Test (GLRT) to detect the range gates affected by WTC, and signal subspace estimation to mitigate the turbine clutter. The performances of the turbine clutter identification and suppression algorithms are also studied by a common evaluation technique of combining clear air wind turbine data and precipitation data.