一种深度学习稀疏单快拍DOA估计方法  

A Deep Learning Approach for Sparse Single Snapshot DOA Estimation

作　　者：朱晗归 冯存前[1] 冯为可 刘成梁 ZHU Hangui;FENG Cunqian;FENG Weike;LIU Chengliang(Air and Missile Defense College,Air Force Engineering University,Xi’an,Shaanxi 710051,China)

机构地区：[1]中国人民解放军空军工程大学,防空反导学院,陕西西安710051

出　　处：《信号处理》2022年第10期2114-2123,共10页Journal of Signal Processing

基　　金：国家自然科学基金(62001507);陕西省高校科协青年人才托举计划(20210106)。

摘　　要：基于信号的稀疏特性,稀疏恢复(SparseRecovery,SR)方法可利用单快拍数据进行相关信号源的高分辨波达方向(DirectionofArrival,DOA)估计。然而,现有SR-DOA模型求解方法存在参数设置困难、运算复杂度高或精度有待提高等问题,实际应用受限。针对上述问题,本文提出平滑L0网络(SmoothedL0 Net,SL0-Net)方法,将基于模型驱动SL0算法和基于数据驱动的深度学习方法相结合,用于SR-DOA模型的求解。首先,建立DOA估计的SR模型,并对用于求解该模型的SL0算法进行分析。然后,根据深度学习框架构建SL0-Net,并基于充足完备的数据集对其网络参数进行训练。最后,利用训练得到的SL0-Net对SR-DOA模型进行求解,获得DOA高分辨估计。仿真结果表明,与现有典型算法相比,所提SL0-Net更适于信号源数目未知条件下的快速高分辨DOA估计。Based on the sparsity of signal,Sparse Recovery(SR)method can use single snapshot data for high-resolution DOA(Direction of Arrival)estimation of correlated signal sources.However,existing methods for solving the SR-DOA model always suffer from the problems of parameter setting difficulty,high computational complexity,or low recovery accu⁃racy,limiting their practical applications.To solve these problems,this paper proposes Smoothed L0 Net(SL0-Net),which combines the model-based SL0 algorithm and the data-driven deep learning method to solve the SR-DOA model.At first,the SR model for DOA estimation is established and the SL0 algorithm used to solve this model is analyzed.Then,based on deep learning framework,SL0-Net is constructed,whose parameters are trained with sufficient and complete da⁃tasets.At last,the trained SL0-Net is used for solving the SR-DOA model,achieving the high-resolution DOA estimation result.Simulation results show that,compared with existing typical algorithms,the proposed SL0-Net is more suitable for fast and high-resolution DOA estimation under the condition of unknown signal source number.

关 键 词：波达方向估计 稀疏恢复 平滑L0范数 深度学习 

分 类 号：V221.3[航空宇航科学与技术—飞行器设计] TN951[电子电信—信号与信息处理]