导向矢量正交分解的离格信号DOA估计

Off-Grid Signal DOA Estimation Based on Orthogonal Decomposition of Steering Vector

当存在离格信号时,网格失配将导致基于压缩感知理论的DOA估计算法估计性能严重下降。为解决这个问题,在对接收数据协方差矩阵进行KR积变换的基础上,提出了一种基于压缩感知理论下的导向矢量正交分解的离格信号DOA估计算法。算法利用信号导向矢量与其一阶导函数矢量间的正交性构建了新的离格信号导向矢量模型,并基于最小二乘法对离格信号的网格偏离量进行估计。在构建稀疏重建模型时,采用ILSSE方法精确估计噪声协方差矩阵,提高了稀疏重建的精度。仿真结果表明,所提算法在不同信噪比和不同的网格间距下对离格信号DOA都有较好的估计精度。

When the off-grid signals appear,the grid mismatching will lead to the serious performance degradation in compressed sensing direction of arrival (DOA)estimation.To address this issue,an off-grid signal DOA estimation algorithm under compressed sensing framework is proposed based on the Khatri Rao transform of received data covariance matrix and the orthogonal decomposition of steering vector.A new steering vector model of off-grid signal is created according to the orthogonality between signal steering vector and its first derivative.The grid deviation is estimated based on the least square theory. To increase the accuracy of sparse reconstruction,the iterative least squares subspace estimation (ILLSE)is adopted to estimate noise covariance matrix in constructing the sparse reconstruction model. The simulation results show that the proposed algorithm has good performance on off-grid signal DOA estimation under different signal to noise ratios and grid spacings.