Effective DOA Estimation Under Low Signal-to-Noise Ratio Based on Multi-Source Information Meta Fusion  

作　　者：Yun Wu Xiukun Li Zhimin Cao 

机构地区：[1]Acoustic Science and Technology Laboratory and the College of Underwater Acoustic Engineering,Harbin Engineering University,Harbin 150001,China [2]Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University),Ministry of Industry and Information Technique,Harbin 150001,China [3]School of Physics and Electronic Engineering,Northeast Petroleum University,Daqing 163318,Heilongjiang,China

出　　处：《Journal of Beijing Institute of Technology》2021年第4期377-396,共20页北京理工大学学报（英文版）

基　　金：the National Natural Science Foundation of China(Nos.11774073 and 51279033).

摘　　要：Efficiently performing high-resolution direction of arrival(DOA)estimation under low signal-to-noise ratio(SNR)conditions has always been a challenge task in the literatures.Obvi-ously,in order to address this problem,the key is how to mine or reveal as much DOA related in-formation as possible from the degraded array outputs.However,it is certain that there is no per-fect solution for low SNR DOA estimation designed in the way of winner-takes-all.Therefore,this paper proposes to explore in depth the complementary DOA related information that exists in spa-tial spectrums acquired by different basic DOA estimators.Specifically,these basic spatial spec-trums are employed as the input of multi-source information fusion model.And the multi-source in-formation fusion model is composed of three heterogeneous meta learning machines,namely neural networks(NN),support vector machine(SVM),and random forests(RF).The final meta-spec-trum can be obtained by performing a final decision-making method.Experimental results illus-trate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estim-ators.Even under low SNR conditions,promising DOA estimation performance can be achieved.

关 键 词：direction of arrival(DOA) signal-to-noise ratio(SNR) information fusion meta-learn-ing spatial spectrum 

分 类 号：TN911.7[电子电信—通信与信息系统]