Variational Bayesian Inference for DOA Estimation Under Impulsive Noise and Non-Uniform Noise
Kun Guo, Liang Zhang, Yingsong Li, Tian Zhou, Jingwei Yin
Abstract
Existing direction of arrival (DOA) estimation approaches are often only considering Gaussian noise or impulsive noise, leading to the performance degradation in the scenario that both noises exist simultaneously. Considering that ambient noise of underwater acoustic array may have different variances due to the large aperture, this paper proposes a robust sparse recovery method based on variational Bayesian inference (VBI) that considers the “heavy-tailed” characteristics of impulsive noise, and the non-uniformity of ambient noise. Student-t distribution and Bernoulli distribution are modeled as impulsive noise in the measurement, and then, the array observed signal is created as a mixture of desired signal, impulsive noise and non-uniform noise. A VBI scheme is constructed to estimate the desired sparse signal to implement DOAs. Results obtained from the numerical simulation and experimental data processing verify the superior performance of the proposed VBI promoting DOA estimation for dealing with impulsive noise and non-uniform noise.