基于协方差矩阵稀疏表示的相干源DoA估计算法

DoA estimation algorithm for coherent signals based on sparse representation of covariance matrix

基于压缩感知理论的波达方向(Direction of Arrival,DoA)估计方法可以在低于奈奎斯特采样率的情况下估计信号参数,是阵列信号处理领域的热点。提出了一种新的基于协方差矩阵稀疏表示的相干源DoA估计算法。该算法首先根据非圆类信源的实值特性,利用改进的虚拟阵列算法进行去相干处理,避免了孔径损失,且减少了运算量。然后利用入射信号在空域的稀疏性,将DoA估计问题转化为空间协方差矩阵的稀疏表示模型,并对其稀疏表示模型进行稀疏重构获得角度估计。通过仿真对不同算法的成功概率进行了研究,结果表明,该算法在处理相干信源时性能优于其它算法,在低信噪比、小角度间隔、小快拍数条件下具有更高的成功概率。

The DoA estimation algorithm based on compressive sensing theory can estimate the signal parameters under Nyquist＇s sampling rates,which is a hotspot in the field of array processing.A novel DoA estimation algorithm base on sparse representation of covariance matrix is proposed for coherent sources.Firstly,considering the real-valued property of non-circular signals,the algorithm uses a improved virtual array algorithm to de-correlate the coherent sources,which can avoid the loss of aperture,and reduce the computational load.Then,utilizing the spatial sparse property of incident signal,the DoA estimation problem is converted to sparse representation model of covariance matrix,and the DoA estimation is achieved by sparse reconstruction of the model.Through simulations,the achievement probability of each algorithms,the results show that the proposed algorithm can outperform the other algorithms in the case of coherent signals,especially under the conditions of low SNR,small angle interval,or small snapshot number.