电讯技术2025，Vol.65Issue(2)：261-268,8.DOI:10.20079/j.issn.1001-893x.240206002
基于改进标签策略与卷积神经网络的离格DOA估计方法
Off-grid DOA Estimation via Convolutional Neural Network with Improved Label Strategy
袁野 1吕昭 1汪淼 1徐步云 1李盼1
作者信息
- 1. 中国人民解放军 32806 部队,北京 100091
- 折叠
摘要
Abstract
In order to estimate the direction of arrival(DOA)of off-grid signals accurately and effectively,a convolutional neural network(CNN)is utilized to extract the depth feature information in the covariance matrix of signal,and an improved labeling strategy is employed to ensure the accuracy and efficiency of the estimation network.Specifically,the tensor composed of the covariance matrix is annotated by labels with decimals,and an improved binary cross-entropy loss function is used to make the proposed decimals labels available for network training.For the multi-label and multi-classification problem corresponding to DOA estimation,the output unit categories and magnitudes of convolutional neural network containing 6-layer structure are used to reconstruct the integer and fractional parts of the DOA of the off-grid signal,respectively.By comparing the simulation results of root mean square error(RMSE)with six typical methods,the proposed method have an excellent performance with RMSE less than 0.5° when SNR=-10 dB.Although it is unable to work properly with fewer snapshots,the proposed method still maintains the performance of RMSE<1° under the condition that the number of snapshots is greater than 8.Meanwhile,the proposed method still has high estimation stability when the number of signals is 5,and the computation speed can reach milliseconds,which takes significantly less time than other methods.关键词
离格DOA估计/人工智能/卷积神经网络/监督学习Key words
off-grid DOA estimation/artificial intelligent/convolutional neural network/supervised learning分类
信息技术与安全科学引用本文复制引用
袁野,吕昭,汪淼,徐步云,李盼..基于改进标签策略与卷积神经网络的离格DOA估计方法[J].电讯技术,2025,65(2):261-268,8.
