云南民族大学学报（自然科学版）2026，Vol.35Issue(1)：107-116,10.DOI:10.3969/j.issn.1672-8513.2026.01.013
CV-CNN与稀疏贝叶斯学习结合的声源定位方法研究
CV-CNN combined with sparse bayesian learning for sound source localization
摘要
Abstract
Most existing underwater target localization algorithms rely on the prior condition that the number of acoustic sources is known.However,in practical applications,the number of sound sources is often difficult or incorrect in advance,so it often leads to reduced positioning accuracy or even failure.Therefore,this paper presents a novel method for sound source localization based on Complex-Valued Convolutional Neural Networks(CV-CNN)and sparse Bayesian learning.First,a neural network learns the relationship between sensor data and sound source counts,then uses it to predict the number of unknown sources.Following this,for sound source localization,the sparse Bayesian learning algorithm locates the target based on the estimated number.The CV-CNN model shows 99.16％accuracy,and the localization error remaining under 1° not only underwater typical conditions but also at a low SNR of-5 dB or with only 100 snapshots.关键词
阵列信号处理/深度学习/离格稀疏贝叶斯学习/DOA估计Key words
array signal processing/deep learning/off-grid sparse bayesian learning/DOA estimation分类
通用工业技术引用本文复制引用
崔晶,邢传玺,魏光春,董赛蒙..CV-CNN与稀疏贝叶斯学习结合的声源定位方法研究[J].云南民族大学学报（自然科学版）,2026,35(1):107-116,10.基金项目
国家自然基金(61761048) （61761048）
云南省基础研究专项面上项目(202101AT070132). （202101AT070132）
