Unitary Low-Rank Matrix Decomposition for DOA Estimation in Nonuniform Noise
Yanghui Zhu, Xianpeng Wang, Liangtian Wan, Mengxing Huang, Wenlong Feng, Juwei Wang · 2018
The problem of direction-of-arrival (DOA) estimation is studied with the existence of uncertain nonuniform noise, and the unitary low-rank matrix decomposition algorithm is proposed. In the proposed method, the complex-valued matrix is turned into real-valued one by using the centrosymmetric characteristic of uniform linear array and a real-valued covariance matrix is obtained with the unknown nonuniform noise. Then a unitary low-rank matrix decomposition method is formulated for removing nonuniform noise covariance by utilizing the matrix completion technique. Once the noise-free real-valued covariance matrix is determined, the unitary subspace-based algorithms can be applied to estimate the DOA. Compared with the existing methods, the proposed method can provide better DOA estimation performance and reduce the computational complexity especially with the limited snapshots and existence of nonuniform noise. Numerical computer simulations are used to verify the effectiveness of the proposed method.