Blind Estimation of Chaotic Spread Spectrum Sequences
混沌直扩信号扩频序列盲估计

Chaotic Direct Sequence Spread Spectrum (CD3S) signal is more nonlinear and more complex than the conventional direct sequence spread spectrum signal. This is the merit of CD3S, but also difficulty of estimation of chaotic spread spectrum sequences. According to the difficulty, a nonlinear Resilient back PROPagation (RPROP) neural network was proposed to estimate the chaotic sequences. The proposed method takes full advantages of the neural network’s nonlinearity. It does not need to search a synchronous point between symbol waveform and chaotic sequences. The coefficient of neural network is used to estimate the chaotic spread spectrum sequences. The simulation results show that the method can estimate the chaotic sequences exactly at low SNR.