Robust Matrix Filling DOA Estimation Algorithm for Sparse Arrays

Sparse arrays are flexible and can reduce inter-array coupling while increasing the array aperture, but traditional DOA estimation based on sparse arrays can lead to angular ambiguity and confusion, which brings problems of poor estimation accuracy and insufficient robustness. In this paper, we propose a robust matrix filling algorithm with weighted truncated singular value projection (WT-SVP) for DOA estimation of sparse arrays, in which weights are assigned according to the size of singular values during the filling iteration to highlight the array information contained in large singular values and reduce the unnecessary weights in small singular values. The traditional singular value projection algorithm is optimized by reducing unnecessary noise information in small singular values. The algorithm can achieve hole information recovery of sparse arrays and make full use of discontinuous array elements, while the WT-SVP filling algorithm achieves high accuracy and high resolution of sparse array DOA estimation and high robustness at low signal-to-noise ratio and low snapshot.