基于状态估计的混沌直扩信号联合均衡与解调

A novel unified equalization and demodulation of chaotic direct sequence spread spectrum signal based on state estimation

针对混沌直接序列扩频信号(以下称混沌直扩信号)通过非理想信道,利用信道和混沌直扩信号的状态方程及其均衡和解调的关联性,提出一种基于状态估计的联合均衡与解调算法.算法采用多扩展卡尔曼滤波(extended kalman filter,EKF)结构,一边对信道均衡,一边估计二进制信息码,二者互为因果,同时进行,不仅可以有效克服非理想信道所带来的多径信道畸变、加性噪声等对信号的影响,还可将掩藏在混沌直扩信号中的原始二进制信息码解调出来,比均衡和解调分离的做法更有效地利用信息,有更好的实时性.仿真结果表明,所提出的算法收敛速度快,能在信道特性未知情况下较好地抵御多径效应和噪声影响,实现了混沌直扩信号在非理想信道条件下的有效可靠传输.

A novel unified algorithm based on state estimation was proposed for equalization and demodulation of chaotic direct sequence spread spectrum （ CD3S） signal through a non-ideal channel. The algorithm uses the state equations of the channel and the CD3S signal,as well as the reciprocal relationship between equalization and demodulation. Multiple extended kalman filters （EKF） were used for equalizing the channel while demodulating the binary message simultaneously by implementing them in reciprocal interaction. This algorithm could not only overcome the impact brought about by non- ideal channel,such as multipath interference and channel noise,but also demodulate the binary message from CD3S signal. This unified approach utilizes the information more adequately and has better real-time performance than independent approaches. Simulation results demonstrated that the proposed algorithm has a fast convergence rate,and is resistant to the multipath effect without knowledge of channel characteristic and the noise,so the efficient and reliable transmission of the CD3S signal through the non-idea channel is realized.