Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks

Abstract

This work proposes an effective high-resolution multisource direction-of-arrival (DOA) estimation method in impulsive noise scenarios based on convolutional neural networks (CNNs). First of all, the array observation matrix is preprocessed and fed into a denoising network to suppress outliers and filter out impulsive noise. Secondly, the denoising network output is fed into a model order selection network to estimate the model order. Next, according to the estimation, the denoising network output is fed into a DOA subnetwork corresponding to the model order in a DOA network to estimate the DOA of each signal. Comprehensive simulations demonstrate that, in the presence of impulsive noise, the proposed method is effective and superior in accuracy and computation speed for multisource DOA estimation. Therefore, it is concluded that CNN can be well generalized for DOA estimation.

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

“…It is well known that these Bayesian algorithms face the challenge of high computational complexity. Recently, deep learning was applied to DOA estimation under impulsive noise in [43], revealing that the DOA prediction ability was reasonably enhanced by utilizing the powerful learning ability of neural networks. However, deep learning algorithms heavily depend on excessive training datasets, and models trained for specific conditions may perform poorly when applied to data models in other new environments.…”