波束域特征融合的浅海水平阵目标方位估计  

Direction of arrival estimation using a horizontal array with beamforming domain feature fusion in shallow water

作　　者：王珍珠 王文博[1,2] 李赫[1,2] 任群言 郭圣明[1,2] WANG Zhenzhu;WANG Wenbo;LI He;REN Qunyan;GUO Shengming(Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190;Key Laboratory of Underwater Acoustic Environment,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049)

机构地区：[1]中国科学院声学研究所,北京100190 [2]中国科学院水声环境特性重点实验室,北京100190 [3]中国科学院大学,北京100049

出　　处：《声学学报》2024年第5期939-955,共17页Acta Acustica

基　　金：国家自然科学基金项目(12204506);中国科学院声学研究所自主部署项目(MBDX202102)资助

摘　　要：针对实际海洋环境影响下水平阵目标方位估计性能降低的问题,提出了一种基于水平阵波束域特征融合的卷积神经网络方位估计方法。首先建立数据计算仿真模型用于生成数据集,利用常规波束形成和最小方差无失真响应两种波束形成器得到波束域特征数据。然后,利用两种波束域特征数据分别训练卷积神经网络模型,仿真和海试数据均表明,训练后的卷积神经网络模型方位估计精度优于常规波束形成、最小方差无失真响应和匹配波束处理,尤其是在端射方向性能提升明显。最后,将两种波束域特征进行前端特征融合后再训练卷积神经网络模型,海试数据测试结果表明,特征融合后的卷积神经网络模型方位估计性能进一步提升,在5°误差范围内的可靠测向概率比单一特征卷积神经网络估计结果高约4%,均方根误差降低约0.2°。A convolutional neural network method for direction of arrival estimation based on beam domain feature fusion of the horizontal line array is proposed to solve the problem of performance degradation of direction of arrival in marine environment.Firstly,a data computational simulation model is built for generating the dataset,and beam-domain data are obtained using both conventional beamforming and minimum variance distortionless response beam formers.Then,the convolutional neural network models are trained separately using the two beam-domain data.Both simulation and sea trial data processing results confirm that the direction of arrival estimation accuracy of the trained models outperforms that of conventional beamforming,minimum variance distortionless response and matched beam processing,especially in the end-fire direction where the performance improvement is obvious.Finally,the two beam-domains features are used to train the convolutional neural network by early-fusion.The sea trial data test results confirm that the direction of arrival estimation performance of the feature-fused convolutional neural network is further improved.The probability of reliable direction estimation within 5°error range is about 4%higher than that the single-feature convolutional neural network estimation result,and the root mean square error is reduced by about 0.2°.

关 键 词：卷积神经网络 水平阵 波达方向估计 波束域 特征融合 

分 类 号：TB566[交通运输工程—水声工程] TP183[理学—物理]