一种基于改进型Logistic映射的混沌信号估算跟踪方法

Method of chaotic signals estimation and track based on improved Logistic map

虽然无先导卡尔曼滤波(UKF)技术在性能上要优于一阶线性化的扩展卡尔曼滤波(EKF)技术,但是对于改进型Logistic混沌映射的扩频通信系统,UKF运算时间长,算法复杂。针对上述缺点以及改进型Logistic映射的泰勒展开式最高项为二阶的特点,提出将二阶EKF运用到接收系统中,该接收系统能精确到泰勒展开式的二阶,达到与UKF相同的性能。相比UKF的复杂算法更加简单,运算速度也更快。仿真实验表明,虽然二阶EKF与UKF的误码率相同,但在运算速度与复杂度方面均优于UKF。

It has been pointed out that chaotic signals could be estimated and tracked by Kalman filter,which solves the problem of chaos synchronization.Unscented Kalman filter（UKF） technique has a better performance than extended Kalman filter（EKF） which is based on the first order linearization.But UKF costs too much time on operation in spread spectrum communication system based on improved Logistic chaotic mapping and its algorithm is complex too.In response to these shortcomings and also because of the improved Logistic mapping＇s highest item of Taylor expansion is second-order,this paper applied the second-order EKF to receiver.It shows that the receiving system can be accurate to the second order Taylor expansion,which has the same performance as the UKF.Comparing with the UKF,second-order EKF is more simple in algorithm and faster in operation.Simulation results show that second-order EKF is better than UKF in computing speed and complexity,while they have the same BER.