2023/11/20 by Ruiyan Cai, Quan Tian · 10 citations
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Algorithm #Artificial intelligence #Artificial neural network #Computer science #Convolutional neural network #Deep learning #Direction of arrival #Direction-of-Arrival Estimation Techniques #Gaussian #Gaussian noise #Impulse noise #Noise (video) #Pattern recognition (psychology) #Robustness (evolution) #Speech and Audio Processing #Speech recognition #Telecommunications
paper · doi:10.1109/tap.2023.3332502
published in IEEE Transactions on Antennas and Propagation 72(2), 2047-2051 (IEEE Antennas & Propagation Society)
openalex publication_date 2023/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
Direction-of-arrival (DOA) estimation methods have been widely and deeply studied in Gaussian noise environments. However, if there is impulsive channel noise, the performance of the method will significantly decline, and reasonable results may not be obtained. Considering that the high performance of model-driven DOA estimation algorithms requires large arrays and more sample data, this communication proposes a two-stage deep convolutional neural network (TSDCN) algorithm for DOA estimation. The first stage suppresses alpha-stable distributed impulsive noise through an adversarial learning network, and the second stage realizes DOA estimation through a deep convolutional neural network. Simulation and real-world experiments show that the TSDCN outperforms most DOA estimation algorithms in terms of robustness and estimation accuracy in impulsive noise environments.