Infinite Weighted p-Norm Sparse Iterative DOA Estimation via Acoustic Vector Sensor Array under Impulsive Noise

Recently, many direction-of-arrival (DOA) estimation techniques based on sparse representation have been proposed. However, these techniques often suffer from performance degradation issues in the presence of impulsive noise. This paper aims to overcome this challenge in conventional sparse-based techniques on an acoustic vector sensor array (AVSA). Firstly, to remove high outliers from the array output data, the output information of the AVSA is weighted by using the infinite norm. To further suppress outliers, a p-order cost function is formulated by extending the Frobenius norm to lower order, and then the expression of the signal power is quantified. Lastly, the DOA is approximated on the signal power by a spectral peak search mechanism. DOA estimation results based on Monte Carlo simulations validate the accuracy and robustness of the proposed techniques herein compared to the current, available methods in the context of intense impulsive noise, low generalized signal–to–noise ratio (GSNR), and a smaller number of snapshots.