One-bit DOA Estimation Using Robust Sparse Covariance Fitting in Non-uniform Noise
Mingyang Chen, Qiang Li, Lei Huang, Lifang Feng, Mohamed Rihan, Deyin Xia · 2022
Accurate direction of arrival (DOA) estimation with one-bit quantized data is of considerable interest in the array signal processing community. This paper addresses a robust one-bit DOA estimation by using sparse covariance fitting in the presence of non-uniform noise whose covariance matrix is not identical diagonal. First, considering arbitrary array structure, a one-bit signal model under non-uniform noise is formulated. Then, with the help of Arcsine law and Khatri-Rao product operation, the unquantized covariance matrix with normalization is column-wise vectorized, then the variances of noise are eliminated by a linear transformation. After that, the one-bit DOA estimation problem is formulated as an optimization of robust sparse covariance fitting which can be solved easily. Experimental results show that the proposed algorithm outperforms the state-of-the-art approaches in terms of root mean square error (RMSE).