基于奇异值阈值和DSS的雷达信号盲分离方法

Blind Source Separation of Noisy Radar Signals Based on Singular Value Threshold and DSS

针对含噪阵列接收模型中盲源分离问题,提出了一种基于奇异值阈值Stein无偏风险估计(SURESVT)和去噪源分离DSS的含噪雷达信号盲分离算法,即SURE-DSS算法。该算法首先采用SURESVT算法替换DSS算法中的奇异值分解,求出观测数据在Stein无偏风险估计原则下的奇异值最优阈值,然后对观测数据的奇异值进行紧缩操作,达到提高信噪比的目的,同时完成观测数据的白化,最后对白化后数据进行盲分离。仿真结果表明,该算法能够在含噪阵列接收模型下对雷达信号进行有效分离。

SURE-DSS, a method for blind separation of radar signals with noise was proposed based on Stein＇s Unbiased Risk Estimate Singular Value Thresholding （SURESVT） and Denoising Source Separation （DSS）. First, SURESVT was used in place of the singular value decomposition of DSS for obtaining the optimum threshold of the singular value for the observed data, under Stein＇s unbiased risk estimation principle. Then, the singular values of the observed data were compressed to improve the signal-to-noise ratio while implementing data whitening. At last, the whitened data was separated blindly. The simulation results show that the proposed method can separate the mixed signals effectively for the array model with noise.