稀疏阵列布阵灵活,增大阵列孔径的同时还能减少阵元间耦合,但基于稀疏阵列的传统波达方向估计会导致角度模糊混叠,带来估计精度差和稳健性不足的问题.针对以上问题,提出一种适用于稀疏阵列波达方向估计的加权截断奇异值投影(weighted truncated singular value projection,WT-SVP)的鲁棒矩阵填充算法.在填充迭代过程中根据奇异值的大小分配权重,突出大奇异值包含的阵列信息,减少小奇异值中不必要的噪声信息,从而优化传统奇异值投影算法.该算法可以实现稀疏阵列的孔洞信息恢复,对不连续阵元充分利用,同时WT-SVP填充算法实现了稀疏阵列波达方向估计的高精度、高分辨以及在低信噪比、低快拍时的高鲁棒性.
Robust matrix completion DOA estimation algorithm for sparse array
The sparse array is flexible and can reduce the coupling between array elements while increasing the array aperture.However,the traditional direction of arrival(DOA)estimation based on sparse array will lead to angle fuzzy aliasing,resulting in poor estimation accuracy and insufficient robustness.In view of the above problems,a robust matrix completion algorithm for sparse array DOA estimation based on weighted truncated singular value projection(WT-SVP)is proposed.In the filling iteration process,weights are allocated according to the size of singular values to highlight the array information contained by large singular values and reduce unnecessary noise information in small singular values,so as to optimize the traditional singular value projection algorithm.The algorithm can realize the hole information recovery of sparse array and make full use of discontinuous elements.Meanwhile,the WT-SVP completion algorithm realizes the high precision,high resolution and high robustness of sparse array DOA estimation at low signal-to-noise ratio and low fast beat.
sparse arraymatrix completionsingular value projectiondirection of arrival(DOA)estimation
万方数据
维普
