High-Resolution Multiple-Source 2D DOA Estimation Using Convolutional Neural Network With Robustness to Array Imperfections
Year: 2024
Type: article
Abstract: In this paper, we present an efficient convolutional neural network (CNN)-based model to estimate both elevation and azimuth arrival angles of multiple sources with high resolution (small source angular separation). The sources are considered closely spaced in both elevation and azimuth up to 0.5° resolution in both directions. Ten sources are supposed to be received by an$8\times 8$antenna array. Compared to multiple signal classification (MUSIC) algorithm and radial basis function neural network (RBFNN), numerical results indicate that the CNN model exhibits notable precision in two-dimensional (2D) direction of arrival (DOA) estimation of multiple-source with high resolution, along with less than one-tenth second execution time. Finally, it maintains its superior performance even in the presence of array imperfections and significantly reduced SNR.
Source: IEEE Access
Authors: Tarek Sallam, Qun Wang, Ahmed M. Attiya
Institutions: Shandong Xiehe University, Electronics Research Institute
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,995
OpenAlex ID: W4403295160