电讯技术2026，Vol.66Issue(9)：1464-1471,8.DOI:10.20079/j.issn.1001-893x.250319001
融合混合注意力与动态调度的残差网络DOA估计
DOA Estimation with a Residual Network Integrating Hybrid Attention and Dynamic Scheduling
摘要
Abstract
To address the challenges of boundary-crossing errors and overfitting commonly encountered in conventional deep learning models for direction-of-arrival(DOA)estimation in array signal processing,the authors propose a residual network model that integrates hybrid attention and dynamic learning rate scheduling.The model takes the spatial covariance matrix of array signals as input and outputs the estimated direction of the signal sources.Built upon a residual neural network framework,the model incorporates an angular constraint strategy to effectively mitigate periodic boundary errors,employs a hybrid attention mechanism to enhance the extraction of key features,applies label smoothing to alleviate overfitting,and adopts a cosine annealing schedule to dynamically adjust the learning rate for improved training performance.Experimental results demonstrate that the proposed model consistently outperforms traditional methods under both uniform and non-uniform linear array conditions with single-source input,achieving the lowest estimation errors across various typical simulation scenarios.关键词
DOA估计/残差网络/混合注意力/标签平滑/动态学习率调度/深度学习Key words
DOA estimation/residual network/hybrid attention/label smoothing/dynamic learning rate scheduling/deep learning分类
信息技术与安全科学引用本文复制引用
杨翰琨,杨文铁,王作帅,徐以东..融合混合注意力与动态调度的残差网络DOA估计[J].电讯技术,2026,66(9):1464-1471,8.基金项目
国家自然科学基金资助项目(52101383) （52101383）
