混沌直扩信号扩频序列盲估计

胡进峰; 郭静波

doi:10.3724/SP.J.1146.2006.02054

混沌直扩信号扩频序列盲估计

doi: 10.3724/SP.J.1146.2006.02054

基金项目:

国家专项重点基金资助项目

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出版历程
- 收稿日期: 2006-12-25
- 修回日期: 2007-06-26
- 刊出日期: 2008-08-19

Blind Estimation of Chaotic Spread Spectrum Sequences

摘要

摘要: 与传统直扩序列相比，混沌扩频序列具有非线性复杂度较高的优点，该优点同时也是盲估计混沌扩频序列的难点。针对这个难点，该文提出了非线性弹性反传神经网络盲估计方法，充分利用非线性神经网络能逼近任意非线性函数的特性，无须搜索信息码和扩频序列之间的同步点，能在较低的信噪比下准确盲估计混沌扩频序列。传统的神经网络使用中，神经网络的有用信息是网络的输出，而该文中则是输出层的权系数。侦察截获的混沌直扩信号同时用作神经网络的输入和期望输出，神经网络收敛后的输出层权系数就是混沌扩频序列的估计值。仿真结果证明了该方法的有效性。
- 混沌直扩; 混沌扩频序列; 盲估计; 弹性反传神经网络
Abstract: 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 networks 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.

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