基于协方差矩阵拟合的宽带无网格波达角估计

Wideband grid-free direction of arrival estimation based on covariance matrix fitting

宽带波达角(Direction of Arrival,DOA)估计是声呐系统阵列信号处理中一个重要的研究方向。文章提出了一种基于相干子空间的改进稀疏与参数方法(Coherent Signal-subspace based Modified Sparse and Parameter Approach,C-MSPA),以实现高精度和高空间方位分辨能力的宽带DOA估计。算法利用聚焦矩阵将各子带上的采样协方差矩阵投影至聚焦频率上。完成聚焦后,文章基于频率选择的范德蒙分解理论对协方差矩阵拟合准则进行改进,使重构的协方差矩阵中包含的DOA信息严格限制在聚焦区域内,最终对重构的协方差矩阵进行范德蒙分解,得到DOA估计值。所提出的算法无需选取正则参数,同时避免了基不匹配问题。仿真和湖上实测数据分析结果表明,所提出的方法实现了高空间方位分辨能力且提高了DOA估计精度。

Wideband direction of arrival(DOA)estimation is an important research subject in sonar systems.In this paper,a coherent signal-subspace based modified sparse and parameter approach(C-MSPA)is proposed to achieve high-accuracy and high spatial azimuth resolution wideband DOA estimation.The proposed algorithm uses the focusing matrix to project the sample covariance matrix in each frequency bin onto the focusing frequency bin.After focusing,the covariance matrix fitting criterion is modified by the frequency-selective Vandermonde decomposition to reconstruct the covariance matrix where the DOAs encoded are restricted in the focusing sectors.Finally,the DOAs are estimated by performing the Vandermonde decomposition on the reconstructed covariance matrix.The proposed algorithm does not need to select regularization parameters and avoids the basis mismatch issue.The results of simulation and lake experiment show that the proposed method achieves high resolution and improves the DOA estimation accuracy.