以提升舰船通信安全性及同步性为出发点，设计基于神经网络的舰船混沌保密通信系统。基于舰船混沌同步保密通信系统一般模型，在系统发射端设计基于径向基函数神经网络（RBFNN）的跟踪器，跟踪舰船信息信号，通过混沌调制模块调制系统的状态量参数，并加密舰船信息信号将其混沌视为载波进行混沌掩盖后，通过信道进行传输，经噪声消除器去除信号噪声后，在混沌解调模块设计基于RBFNN的同步控制器，输出同步解调后的原始舰船信息信号传输至接收端，实现舰船信息保密同步通信。实验结果显示，该系统加密后，信息可有效掩盖信号真实情况，信号去噪后可还原信号本真，解调后可在接收端实现高精度的信号还原；且该系统应用后的信息可有效防止攻击者窃取。
Starting from improving the security and synchronization of ship communication, this paper studies a ship chaotic secure communication system based on neural networks. Based on the general model of ship chaotic synchronous secure communication system, a tracker based on radial basis function neural network (RBFNN) is designed at the transmitting end of the system to track ship information signals. The state parameter of the system is modulated by a chaotic modulation module, and the ship information signal is encrypted to treat its chaos as a carrier wave for chaotic masking. After that, it is transmitted through the channel, and the signal noise is eliminated by a noise canceller, Design a synchronization controller based on RBFNN in the chaotic demodulation module, output the original ship information signal after synchronization demodulation, and transmit it to the receiving end to achieve secure synchronization communication of ship information. The experimental results show that the encrypted information of the system can effectively mask the true situation of the signal, the signal can be restored to its true state after denoising, and high-precision signal restoration can be achieved at the receiving end after demodulation. And the information after the application of the system can effectively prevent attackers from stealing.
2024,46(5): 176-179 收稿日期：2023-10-23
DOI：10.3404/j.issn.1672-7649.2024.05.033
分类号：U665
基金项目：2019年大数据与人工智能福建省高校重点实验室（GXKYSY201901）；2023年闽南科技学院级科研团队(MKKYTD202302)；2022年福建省级一流本科专业建设点（SJZY-2022-01）；2020年福建省级虚拟仿真实验教学一流课程（SJKC-2020-03）
作者简介：彭青梅(1989-),女,硕士,讲师,研究方向为信息安全。
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