关键词：传感器；混沌信号与系统；噪声比；SNR
摘 要：Chaotic signals and systems offer the potential of increased security in digital communications. However, most of the proposed approaches either lack robustness at low signal-to-noise ratios (SNRs) (due to the difficulty of synchronizing chaotic signals) or provide a much worse performance than classical techniques based on sinusoidal carrier functions. In this chapter we show how asymptotically optimal estimators, developed for the estimation of chaotic signals generated by discrete chaotic maps and corrupted by additive white Gaussian noise (AWGN), can be applied to improve the performance of digital chaotic communication schemes. First of all, after a brief review of discrete- time chaotic maps and sequences, we derive the optimal maximum likelihood (ML) estimator for this problem. Unfortunately, its computational cost grows exponentially with the length of the chaotic sequence, thus rendering it un-feasible for moderate/large sequences. Therefore, asymptotically optimal esti-mators, based on well-known signal processing techniques, such as censoring approaches or the Viterbi algorithm (VA), with a reduced computational cost, are developed. Finally, we show how these methods can be applied to improve the performance of digital chaotic communications schemes based on the iter- ation of discrete-time chaotic maps, focusing on a recently proposed symbolic coding technique based on backward iteration.