基于非扩张映射及SOM进行特征选择的DOA估计  

DOA estimation based on non-expansive mapping and self-organizing neural networks for feature selection

作　　者：谭秀辉[1] 白艳萍[1] 王鹏[1] 胡红萍[1] 程蓉 续婷[1] Tan Xiuhui;Bai Yanping;Wang Peng;Hu Hongping;Cheng Rong;Xu Ting(School of Mathematics,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学数学学院,太原030051

出　　处：《电子测量技术》2023年第9期189-196,共8页Electronic Measurement Technology

基　　金：国家自然科学基金面上项目(61774137,51875535,61927807);山西省回国留学人员科研项目(2021-108);山西省自然科学基金(202103021224195,202103021224212,202103021223189,20210302123019)项目资助。

摘　　要：为进一步研究窄带水声信号特征与波达方向(DOA)的映射关系,在基于三层自组织神经网络映射对声信号特征向量进行拓扑排序的基础上,提出了结合区域Lipschitzs系数及局部Lipschitzs系数进行改进的DOA估计模型。该方法通过对信号特征与波达角所形成的映射进行非扩张映射检验,即对区域李普希兹系数进行讨论并对映射的优劣进行评判,以自组织神经网络为训练器,依据特征层拓扑排序并结合局部Lipschitzs系数构建基于1-邻域的综合DOA估计法则,从而改进了DOA估计系统。仿真实验结果显示该方法所选择特征用于对DOA的估计效果更优,平均误差、方差均在10-2以内;在信噪比(SNR)从20 dB下降到2 dB的情况下,对照其他常用DOA估计算法,估计结果同时显示出良好的鲁棒性。To further study the mapping relationship between narrowband hydroacoustic signal features and the direction of the arrival(DOA),an improved DOA estimation model combining regional Lipschitzs coefficients and local Lipschitzs coefficients is proposed based on the topological ordering of acoustic signal feature vectors based on threelayer self-organizing neural network mapping.This method is used to check the non-expansive mappings formed by the mapping of signal features to angles of arrival,which is a discussion of the regional Lipschitz coefficients as well as a judgment on the superiority of the mapping,using a self-organizing neural network as trainer,based on the topological ordering of feature layers,and combined with local Lipschitzs coefficients to construct an integrated DOA estimation law based on the 1-neighborhood-rules.The simulation experimental results shows that the method is effective in estimating the angle of direction of arrival,with the average error and variance within 10-2 degree;the estimation results also shows good robustness against other commonly used DOA estimation algorithms,when the signal-to-noise ratio(SNR)decreased from 20 dB to 2 dB.

关 键 词：波达方向估计 三层自组织神经网络 特征拓扑排序 非扩张映射 1-邻域估计法则 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]