基于散射中心的风电机RCS数据压缩重构

Wind Turbine RCS Data Compression and Reconstruction Based on Scattering Center

为解决风电机对雷达信号干扰的实时处理问题,必须将数据量超过十亿量级的风电机全姿态雷达散射截面(Radar Cross Section,RCS)数据进行有效压缩重构。针对传统阈值离散傅里叶变换(Threshold Discrete Fourier Transform,TDFT)由于阈值选取困难而压缩重构效果不佳的问题,避开阈值的选取问题,基于电磁散射理论,提出了一种采用风电机散射中心参数集表征大量RCS数据的思想。基于几何绕射理论散射中心模型,采用总体最小二乘-旋转不变技术信号参数估计(Total Least Squares-Estimated Signal Parameters via Rotational Invariance Technique,TLS-ESPRIT)法,求解了由风电机RCS数据所构成的两个子向量间的旋转因子矩阵,进而获取了风电机一维散射中心参数集,最终实现风电机RCS数据的有效压缩重构。以金风77/1500型风电机为算例。结果表明,与TDFT相比,在同一重构误差范围内,基于散射中心的压缩重构方法数据压缩比最高可达31.25。

In order to solve the real-time processing problem of wind turbine interference to radar signals,it is necessary to effectively compress and reconstruct the wind turbine Radar Cross Section(RCS)with a data volume exceeding one billion.Aiming at the problem that the traditional Threshold Discrete Fourier Transform(TDFT)is difficult to realize wind turbine RCS data compression and reconstruction due to the difficulty of threshold selection,and based on the theory of electromagnetic scattering,this paper avoids the threshold selection and proposes a method to characterize a large number of RCS data using the wind turbine scattering center parameter set.Based on the Geometrical Theory of Diffraction scattering center model,the Total Least Squares-Estimated Signal Parameters via Rotational Invariance Technique(TLS-ESPRIT)method was used to solve the rotation factor matrix between two sub-vectors which were composed of wind turbine RCS data.Then,the wind turbine one-dimensional scattering center parameter set was obtained.Finally,the effective compression and reconstruction of wind turbine RCS data were realized.Taking Jin Feng 77/1500 wind turbine as an example,the results show that compared with TDFT,within the same reconstruction error range,the data compression ratio is up to 31.25 based on scattering center.