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提出了一种基于强跟踪滤波器的混沌保密通信方法. 在发送端, 混沌映射和信息符号被建模成非线性状态空间模型, 信息符号被加性混沌掩盖或乘性混沌掩盖调制, 然后通过信道输出. 在接收端, 驱动信号被接收, 使用带有贝叶斯分类器(信息符号估计)的强跟踪滤波器算法动态地恢复信息符号. Logistic混沌映射的仿真表明, 当信息符号为二进制编码时, 不管是加性混沌掩盖调制还是乘性混沌掩盖调制, 强跟踪滤波器均能较好地从混沌信号中恢复信息符号. 与扩展卡尔曼滤波器相比, 由于卡尔曼滤波器对于离散的信息符号跟踪能力差, 混沌映射中信息符号难以恢复, 比特误码率高. 因此, 这种基于强跟踪滤波器的混沌保密通信方法是有效的.Chaotic secure communication is an active research field of chaotic application. A novel method for chaotic secure communication is proposed based on strong tracking filter (STF) in this study. STF is an extended Kalman filter with suboptimal fading factors, especially suitable for estimating the state and parameter of nonlinear time-varying stochastic systems. The main idea of the proposed method is summarized below. At the emitting end, the chaotic mapping and the information symbol are modeled as a nonlinear state space model, and the information symbol is modulated by additive chaos masking or multiplicative chaos masking and then is outputted through the channel. At the receiving end, the driving signal is received, and the message symbol is recovered dynamically by STF with Bayesian classifier. Simulation tests of the logistic chaotic mapping show that STF can restore the information symbols in chaotic signals when information symbols are binary code, with either additive or multiplicative chaos masking modulation. Compared with STF, the conventional Kalman filter has poor ability to track the discrete information symbol. It is difficult to restore the information symbols in the chaotic mapping, and the bit error rate is high. Therefore, the STF-based chaotic secure communication method is effective.
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