现代电子技术2024，Vol.47Issue(3)：1-6,6.DOI:10.16652/j.issn.1004-373x.2024.03.001
基于深度学习和改进证据理论的海上多源舰船信息融合识别方法
Offshore multi-source ship information fusion recognition method based on deep learning and improved evidence theory
摘要
Abstract
In view of the low target recognition accuracy based on single ship information in complex environment and ineffective fusion recognition of high-conflict multi-source ship information,a method of offshore multi-source ship information fusion recognition method based on deep learning and improved evidence theory is proposed.This paper mainly starts from two aspects.The deep learning efficient feature learning ability is used to carry out more accurate classification and identification.Then,the improved evidence theory is used to fuse the multiple evidence bodies efficiently and correctly.The results of high paradox evidence fusion have higher probability assignment value than those of the other fusion methods.In addition,under different SNR(signal to noise ratio)conditions,the single mode recognition method and the text fusion recognition method were tested.The recognition performance of the proposed method is still 6.53%higher than the average level of the single mode recognition method in the noise environment.Therefore,the proposed fusion recognition method can improve the accuracy and robustness of ship target recognition system.关键词
改进D-S证据理论/深度学习/信息融合/目标识别/舰船目标/融合识别Key words
improved D-S evidence theory/deep learning/information fusion/target recognition/ship target/fusion recognition分类
信息技术与安全科学引用本文复制引用
任秉旺,王肖霞,吉琳娜,杨风暴..基于深度学习和改进证据理论的海上多源舰船信息融合识别方法[J].现代电子技术,2024,47(3):1-6,6.基金项目
国家自然科学基金项目(61972363) （61972363）
