Title: Iterative Reweighted DOA Estimation for Impulsive Noise Processing Based on Off-Grid Variational Bayesian Learning
Abstract: The performance of acoustic source localization with array system is limited by impulsive noise such as electromagnetic interference, car ignitions, bursting, and so on. The impulsive noise decays with heavy-tailed distribution which can be considered as outliers. In order to alleviate the performance degradation of traditional direction-of-arrival (DOA) estimation with impulsive noise, a novel iterative reweighted variational Bayesian learning algorithm based on off-grid model (OG-WVBL) is proposed. OG-WVBL employs impulsive noise as two independent components and models directly the outliers with sparse distribution in the time domain. OG-WVBL utilizes two iterative VBL to reconstruct signal and outliers matrix and then retrieves the DOA. Then, OG-WVBL also introduces the iteratively reweighted strategy to hyperparameters so that the more importance is given to those hyperparameters with non-zero entries over others which can encourage sparsity and achieve the consistent convergence. With the iteratively reweighted strategy, OG-WVBL can automatically identify the number of sources without any prior knowledge. Moreover, the proposed algorithm can use a coarse sampling grid to achieve the accurate DOA estimation with the off-grid model. The experiments and simulation results show that OG-WVBL possesses robust performance and outperforms several existing algorithms under impulsive noise environment.