Sparse Array DOA Estimation Based on Higher-Order Statistics
Zhigang Zhou, Baixiao Chen, Yan Zhang · 2020
Sparse arrays are used in the field of array signal processing because of their advantages, such as larger aperture and lower array mutual interference. The nested array and coprime array are sparse arrays that identify O(N2) sources, where N is the number of array elements. Many algorithms have been introduced to such identification recently. The method of high-order statistics can obtain better performance than the second-order statistics and solve many problems that the second-order statistics cannot explain. Moreover, the high-order statistics of additive white Gaussian noise is 0, which can suppress the Gaussian white noise automatically. So in this paper, it is possible to combine higher-order statistics with the MUSIC algorithm (HOS-MUSIC) on nested arrays and coprime arrays. Compared with the direct MUSIC algorithm, the HOS-MUSIC algorithm can also identify the two closely spaced sources without ambiguity in the two sparse arrays, and the RMSE of the HOS-MUSIC algorithm on sparse arrays is smaller. The HOS-MUSIC algorithm will be shown that there are more advantages in the sparse arrays DOA estimation. At the end of the paper, we also consider the use of nested arrays and coprime arrays for the case where the number of spaced sources is more than N.