基于稀疏贝叶斯学习的低信噪比DOA估计算法  

Sparse Bayesian Learning for DOA estimation at low SNR

作　　者：蒋留兵 荣书伟 车俐 JIANG Liubing;RONG Shuwei;CHE Li(School of Computer and Information Security,Guilin University of Electronic Technology,Guilin 541004,China;School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学计算机与信息安全学院,广西桂林541004 [2]桂林电子科技大学信息与通信学院,广西桂林541004

出　　处：《桂林电子科技大学学报》2019年第3期218-222,共5页Journal of Guilin University of Electronic Technology

基　　金：国家自然科学基金(61561010);广西自然科学基金(2017GXNSFAA198089);广西重点研发计划(桂科AB18126003,AB16380316)

摘　　要：针对波达方向估计算法在低信噪比情况下DOA估计精度普遍不高的问题,提出了一种基于伪噪声重采样技术和求根稀疏贝叶斯学习的离格模型下DOA估计算法。利用生成的伪随机噪声对数据矩阵进行多次重采样,结合求根稀疏贝叶斯学习和局部性能测试去除DOA估计产生的异常值,对所得DOA估计结果进行筛选。仿真结果表明,该算法在低信噪比情况下具有较高的估计精度,是一种有效的DOA估计算法。Aiming at the problem that DOA estimation accuracy is generally not high under the condition of low SNR,the DOA estimation method based on pseudo noise resampling technique and rooted sparse Bayesian learning is proposed.The method firstly resamples the data matrix by using the generated pseudo-random noise,and then combines the root sparse Bayesian learning and local performance test to remove the outliers generated by the DOA estimation and filter the obtained DOA estimation results.The simulation results show that the proposed method has high estimation accuracy under low SNR,which indicates that the method is an effective DOA estimation algorithm.

关 键 词：波达方向(DOA)估计 稀疏贝叶斯学习 伪噪声重采样 低信噪比 

分 类 号：TN911.7[电子电信—通信与信息系统]