Name
Abstract | ||
|---|---|---|
For a nested L-shaped array (N-LsA) composed of two orthogonal nested subarrays, the self-difference co-array of each nested subarray is hole-free, whereas cross-difference co-arrays between subarrays have holes. Due to the existence of holes, virtual cross-correlation matrices with increased degree of freedoms (DOFs) can not be constructed from cross-difference co-arrays, which will degrade the performance of direction of arrival (DOA) estimation. To overcome this problem, a high resolution two-dimensional (2-D) DOA estimation algorithm is exploited for N-LsA in this paper. Specifically, by using oblique projection operators, filled cross-difference co-arrays can be achieved by filling the holes, and virtual cross-correlation matrix will be obtained. Then the virtual correlation matrix of the N-LsA, which consists of virtual cross-correlation matrices and virtual autocorrelation matrices given by filled self-difference co-arrays, is reconstructed for 2-D DOA estimation. Additionally, the proposed algorithm contains an automatic angle-pairing procedure and can handle underdetermined DOA estimation. The estimation error, Cramér-Rao bound and computational complexity are derived. Simulation results show that the proposed algorithm offers substantial performance improvement over the existing algorithms. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.dsp.2019.102623 | Digital Signal Processing |
Keywords | Field | DocType |
2-D DOA estimation,Matrix reconstruction,Nested array,L-shaped array,Oblique projection operator | Oblique projection,Underdetermined system,Pattern recognition,Direction of arrival,Matrix (mathematics),Algorithm,Artificial intelligence,Covariance matrix,Mathematics,Autocorrelation,Computational complexity theory,Performance improvement | Journal |
Volume | ISSN | Citations |
98 | 1051-2004 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunlong Yang | 1 | 1 | 0.35 |
| Xing-Peng Mao | 2 | 20 | 23.68 |
| Yuguan Hou | 3 | 2 | 2.40 |
| Guojun Jiang | 4 | 1 | 0.69 |