Direction Finding by Covariance Matrix Sparse Representation With Sensor Gain and Phase Uncertainties in Unknown Non-Uniform Noise
Yunfei Fang, Shengqi Zhu, Yongchan Gao · IEEE Wireless Communications Letters · 2020
The perfectly partly calibrated antenna array is a frequently assumption in most of the existing array gain/phase calibration methods. In practice, however, the partly calibrated array is usually not available. In this letter, a tail optimization method for direction finding with unknown gains and phases in the presence of spatially non-uniform noise is proposed. Specifically, the unknown gain/phase entry is firstly merged into the signal power by using the sparse representation. Subsequently, a tail optimization method that can significantly suppress the occurrence of pseudo-peaks is designed to determine the signal DOAs without a priori information of unknown sensor gain and phase errors. In addition, the spatially non-uniform noise can be removed by a linear transformation to improve the robustness against the noise. Numerical simulations examples are presented to demonstrate the effectiveness and superior performance of the proposed approach over the other existing counterparts.