A method of chaotic secure communication based on strong tracking filter

Abstract

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.