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OACSTPCD

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.