Blind Demodulation of Chaotic Direct Sequence Spread Spectrum Signals Based on Particle Filters
AbstractApplying the particle filter (PF) technique, this paper proposes a PF-based algorithm to blindly demodulate the chaotic direct sequence spread spectrum (CDS-SS) signals under the colored or non-Gaussian noises condition. To implement this algorithm, the PFs are modified by (i) the colored or non-Gaussian noises are formulated by autoregressive moving average (ARMA) models, and then the parameters that model the noises are included in the state vector; (ii) the range-differentiating factor is imported into the intruder’s chaotic system equation. Since the range-differentiating factor is able to make the inevitable chaos fitting error advantageous based on the chaos fitting method, thus the CDS-SS signals can be demodulated according to the range of the estimated message. Simulations show that the proposed PF-based algorithm can obtain a good bit-error rate performance when extracting the original binary message from the CDS-SS signals without any knowledge of the transmitter’s chaotic map, or initial value, even when colored or non-Gaussian noises exist.
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Li, T.; Zhao, D.; Huang, Z.; Liu, C.; Su, S.; Zhang, Y. Blind Demodulation of Chaotic Direct Sequence Spread Spectrum Signals Based on Particle Filters. Entropy 2013, 15, 3877-3891.
Li T, Zhao D, Huang Z, Liu C, Su S, Zhang Y. Blind Demodulation of Chaotic Direct Sequence Spread Spectrum Signals Based on Particle Filters. Entropy. 2013; 15(9):3877-3891.Chicago/Turabian Style
Li, Ting; Zhao, Dexin; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhang, Yimeng. 2013. "Blind Demodulation of Chaotic Direct Sequence Spread Spectrum Signals Based on Particle Filters." Entropy 15, no. 9: 3877-3891.