Abstract : To mitigate the significant degradation in direction of arrival (DOA) estimation performance caused by time-varying axial deviation (TVAD) in acoustic vector sensor (AVS) array under non-uniform noise, a two-step least squares fitting (TSLSF) technique is presented in this paper. Firstly, a model for the AVS array incorporating TVAD is established by introducing axial deviation parameters into datasets from various sub-time periods (STPs). Then, to treat the noise vectors of each channel in the AVS array as virtual sparse signals, a novel AVS array manifold matrix is formulated. After that, to estimate the TVAD matrix, sparse signals, and noise vector, two cost functions, which fit the signal subspace and noise subspace, as well as the interference and noise covariance matrix, respectively, are constructed based on the principle of weighted least squares. Moreover, their analytical expressions were derived. Furthermore, to handle the effects of TVAD on DOA estimation performance over the entire observation period, the focusing technology is adopted to transform datasets with TVAD from different STPs into the de- sired dataset. Simulation experiments confirmed the effectiveness and resilience of the proposed method using an AVS array in conjunction with TVAD in the presence of non-uniform noise.
Index terms : Direction of arrival (DOA) estimation, Non-uniform noise, Time- varying axial deviation (TVAD), Acoustic vector sensor (AVS) array