DOA Estimation by Jointly Exploiting L1-SVD and Enhanced Spatial Smoothing in Coherent Environment

As a sparse-based direction of arrival (DOA) estimation algorithm, the L1-singular value decomposition (SVD) algorithm is widely used to measure the orientation of targets. In real measurements, the coherent environment that often arises due to multipath propagation leads to the deterioration of the noise immunity and estimation accuracy of the L1-SVD algorithm. Although the decoherence of L1-SVD can be enhanced by introducing spatial smoothing after SVD, which is called SS-L1-SVD, the algorithm does not fully utilize the available information in the observed data. In this article, we propose a new method called L1-enhanced spatial smoothing decomposition (ESSD). ESSD combines spatial smoothing with matrix decomposition by utilizing the relationship among the covariance matrix and the left singular matrix and the singular value matrix. ESSD not only improves the decoherence ability of the algorithm but also makes full use of the information in the observed data and reduces the computational complexity, which makes the algorithm more practical than the traditional algorithms in real measurements. In order to further verify the performance of the new algorithm, we not only performed simulation experiments but also designed a physical experimental platform that can be used for DOA estimation and constructed a real coherent environment caused by multipath propagation and performed physical experiments. The results of simulation and physical experiments show that the L1-ESSD algorithm reduces the error by about 1° and the computation time by about 8 s compared with the conventional L1-SVD algorithm.