混沌通信的非线性N-shift加密与迭代学习辨识算法

摘要

A nonlinear N-shift masking scheme is proposed to enhance the security of chaos communication system where the information signal to be transmitted is over a finite time interval. An iterative learning identification method is presented for recovering the masked information signal in the receiver. The proposed fuUy-saturated learning law is adopted and the sufficient condition for the convergence is derived by using the contraction mapping analysis tool. The effectiveness of the masking scheme and the iterative learning identification algorithm is demonstrated with Chua's chaos circuit with the secure communication scheme.%为增强混沌通信系统的保密性,讨论了非线性N-shift加密方案,从提高信号掩盖的非线性程度方面增强了混沌通信的保密性.利用迭代学习辨识方法来实现解密,完全重建信息信号,理论分析给出了学习算法关于初态误差和输出误差的鲁棒性和收敛性,推导出了学习算法关于非线性掩盖的收敛的充分条件.利用以上迭代学习辨识技术对采用复合非线性掩盖技术的蔡氏混沌通信系统进行仿真,结果说明迭代学习辨识算法可以完全重建信息信号,复合非线性掩盖具有高保密性.