Method of chaotic signals estimation and track based on improved logistic map
一种基于改进型Logistic映射的混沌信号估算跟踪方法

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. It responsed to these shortcomings and also because of the improved Logistic mapping's highest item of Taylor expansion is second-order, this paper proposed so applying the second-order EKF to receiver. It is showed 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.