电讯技术2024，Vol.64Issue(4)：497-503,7.DOI:10.20079/j.issn.1001-893x.220508001
基于深度卷积网络的二维波达方向估计方法
2D Direction of Arrival Estimation Based on Deep Convolutional Network
袁野 1张伟科 2许左宏3
作者信息
- 1. 中国人民解放军 32806 部队,北京 100091
- 2. 中国人民解放军 96901 部队,北京 100094
- 3. 军事科学院系统工程研究院,北京 100141
- 折叠
摘要
Abstract
To improve the real-time and convenient feature of the direction of arrival(DOA)estimation technology,a deep convolution network(DCN)is proposed.The covariance matrix of received signal obtained from the uniformed circular array(UCA)is regarded as an image which contains the real part channel and imaginary part channel.By using the covariance matrix as the input tensor of the convolutional network,it is possible to extract the subtle feature of the DOA implied in the covariance matrix,therefore,the DOA information of multi-signal can be estimated quickly and precisely.The simulation results show that the proposed DCN can achieve the DOA estimation of two dimensional signals well.Compared with the traditional method based on the sub-space calculation,the proposed network can obtain more accurate result and the algorithm is relatively stable,therefore,the network has some potential applications in engineering.关键词
均匀圆阵(UCA)/波达方向(DOA)估计/深度卷积网络(DCN)/人工智能/图像分类Key words
uniformed circular array(UCA)/direction of arrival(DOA)estimation/deep convolutional network(DCN)/artificial intelligence/image classification分类
信息技术与安全科学引用本文复制引用
袁野,张伟科,许左宏..基于深度卷积网络的二维波达方向估计方法[J].电讯技术,2024,64(4):497-503,7.
