Work

Residual Neural Network with Attention Mechanism for Direction-of-Arrival Estimation in Low SNR

Year: 2025

Type: conference-paper

Abstract: In this paper, we propose an improved residual neural network (ResNet) based on an attention mechanism for estimating the direction of arrival (DOA) of linear arrays. The real, imaginary, and phase components of the covariance matrix are used as inputs to an auto-encoder (AE), whose output is then introduced into a cascaded ResNet for DOA estimation. To enhance estimation accuracy, an attention mechanism is integrated into the ResNet, effectively assigning greater weight to the most relevant features. Experimental results demonstrate the proposed method's superior DOA estimation performance compared to existing methods, especially in low SNR.

Source: International Conference on Generative Artificial Intelligence and Information Security (GAIIS)

DOI: 10.1145/3728725.3728819

Authors: Mingyang Li, Pengpeng Li, Rui Wang, Weihua Mou

Institutions: National University of Defense Technology, Chongqing Metrology Quality Inspection and Research Institute

Language: English

Topic: Direction-of-Arrival Estimation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4411010171

FWCI: 0.976

Cites: 19

Cited by: 1

Related to: 10