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The extensive deployment of wind farms has significantly impacted the detection capabilities of space–air bistatic radar (SABR) systems. Although space–time adaptive processing techniques are available, their performance is significantly degraded, and even unable to suppress clutter. This paper explores the geometric configuration of the SABR system and the selection of detection areas, establishing a space–time clutter model that addresses the effects of wind turbine clutter (WTC). Expressions for spatial and Doppler frequencies have been derived to deeply analyze the characteristics of clutter spreading. Building on this, the paper extends two-dimensional space–time data to three-dimensional azimuth–elevation–Doppler data. It proposes a three-dimensional space–time multi-beam (STMB) strategy incorporating the Ordering Points to Identify the Clustering Structure (OPTICS) clustering algorithm to suppress WTC effectively. This algorithm selects WTC samples and applies OPTICS clustering to the clutter-suppressed data to achieve this effect. Simulation experiments further verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The extensive deployment of wind farms has significantly impacted the detection capabilities of space–air bistatic radar (SABR) systems. Although space–time adaptive processing techniques are available, their performance is significantly degraded, and even unable to suppress clutter. This paper explores the geometric configuration of the SABR system and the selection of detection areas, establishing a space–time clutter model that addresses the effects of wind turbine clutter (WTC). Expressions for spatial and Doppler frequencies have been derived to deeply analyze the characteristics of clutter spreading. Building on this, the paper extends two-dimensional space–time data to three-dimensional azimuth–elevation–Doppler data. It proposes a three-dimensional space–time multi-beam (STMB) strategy incorporating the Ordering Points to Identify the Clustering Structure (OPTICS) clustering algorithm to suppress WTC effectively. This algorithm selects WTC samples and applies OPTICS clustering to the clutter-suppressed data to achieve this effect. Simulation experiments further verify the effectiveness of the algorithm.

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In this article, a spectral polarimetric filter named moving spectral depolarization ratio (MsDR) filter is proposed to mitigate the effects of clutter and noise for dual-polarization weather radar without cross-polar measurements. The filter is based on a spectral polarimetric variable termed as spectral depolarization ratio (sDR). sDR can be estimated by any polarimetric weather radar, making the MsDR filter widely applicable. Taking advantage of the spectral polarimetric features and the range-Doppler continuity of precipitation, the MsDR filter is implemented in the range-Doppler spectrogram. The performance of the MsDR filter is assessed using data collected by 1) an X-band full-polarimetric radar interfered by narrow-band clutter (both stationary and moving) and sidelobe wind turbine clutter; 2) a C-band operational dual-polarization radar affected by radio frequency interference. In addition, the implementation of the MsDR filter is straightforward, and the computational complexity is relatively low. Hence, the MsDR filter has great potential to be applied in real-time for operational radar systems at different frequencies.

This paper addresses the mitigation of wind turbine clutter (WTC) in weather radar data in order to increase the performance of existing weather radar systems and to improve weather analyses and forecasts. We propose a novel approach for this problem based on signal separation algorithms. We model the weather signal as group sparse in the time–frequency domain; in parallel, we model the WTC signal as having a sparse time derivative. In order to separate WTC and the desired weather returns, we formulate the signal separation problem as an optimization problem. The objective function to be minimized combines total variation regularization and time–frequency group sparsity. We also propose a three-window short-time Fourier transform for the time–frequency representation of the weather signal. To show the effectiveness of the proposed algorithm on weather radar systems, the method is applied to simulated and real data from the next-generation weather radar network. Significant improvements are observed in reflectivity, spectral width, and angular velocity estimates. [ABSTRACT FROM AUTHOR]