Identification of Chaotic Systems with Application to Chaotic Communication

We propose and develop a novel method to identify a chaotic system with time-varying bifurcation parameters via an observation signal which has been contaminated by additive white Gaussian noise.This method is based on an adaptive algorithm,which takes advantage of the good approximation capability of the radial basis function neural network and the ability of the extended Kalman filter for tracking a time-varying dynamical system.It is demonstrated that,provided the bifurcation parameter varies slowly in a time window,a chaotic dynamical system can be tracked and identified continuously,and the time-varying bifurcation parameter can also be retrieved in a sub-window of time via a simple least-square-fit method.     

基于UKF的多用户混沌通信

为克服信道噪声、系统参数误配及多用户干扰对混沌通信系统的影响,本文组合不同的状态空间模型并结合盲提取算法,提出了一种双无先导卡尔曼滤波器 (dual unscented Kalman filter, DUKF),以实现多用户的混沌通信.仿真结果表明,在多输入多输出信道的多用户通信环境下,该算法有较快的收敛速度,并能有效地实现多用户的混沌通信. 关键词： 混沌通信 多输入多输出 双无先导卡尔曼滤波器 盲提取     

基于Huber的高阶容积卡尔曼跟踪算法

为改善高阶容积卡尔曼滤波算法的滤波精度和鲁棒性, 提出了一种新的基于Huber的高阶容积卡尔曼滤波算法. 在采用统计线性回归模型近似非线性量测模型的基础上, 利用Huber M 估计算法实现状态的量测更新. 进一步结合高阶球面-径向容积准则的状态预测模块构成基于 Huber的高阶容积卡尔曼跟踪算法. 重点分析了Huber代价函数的调节因子对算法跟踪性能的影响. 通过对纯方位目标跟踪和再入飞行器跟踪两个实例验证了所提算法的跟踪性能优于传统高阶容积卡尔曼滤波算法.     

Filtering noisy chaotic signal via sparse representation based on random frame dictionary

The denoising problem of impure chaotic signals is addressed in this paper. A method based on sparse representation is proposed, in which the random frame dictionary is generated by a chaotic random search algorithm. The numerical simulation shows the proposed algorithm outperforms those recently reported alternative denoising methods.     

Dispersion compensation in an open-loop all-optical chaotic communication system

The optical chaotic communication system using open-loop fiber transmission is studied under strong injection conditions. The optical chaotic communication system with open-loop configuration is studied using fiber transmission under strong injection conditions. The performances of fiber links composed of two types of fiber segments in different dispersion compensation maps are compared by testing the quality of the recovered message with different bit rates and encrypted by chaotic modulation (CM) or chaotic shift keying (CSK). The result indicates that the performance of the pre-compensation map is always worst. Two types of symmetrical maps are identical whatever the encryption method and bit-rate of message are. For the transmitting and the recovering of message of lower bit rate (1 Gb/s), the post-compensation map is the best scheme. However, for the message of higher bit rate (2.5 Gb/s), the parameters in communication system need to be modified properly in order to adapt to the high-speed application. Meanwhile, two types of symmetrical maps are the best scheme. In addition, the CM method is superior to the CSK method for high-speed applications. It is in accordance with the result in a back-to-back configuration system.     

Gradient method for blind chaotic signal separation based on proliferation exponent

A new method to perform blind separation of chaotic signals is articulated in this paper, which takes advantage of the underlying features in the phase space for identifying various chaotic sources. Without incorporating any prior information about the source equations, the proposed algorithm can not only separate the mixed signals in just a few iterations, but also outperforms the fast independent component analysis(FastICA) method when noise contamination is considerable.     

开环全光混沌通信系统中的光纤信道结构

根据外光反馈半导体激光器及光纤传输方程,构建了基于光纤信道的开环全光混沌通信系统.考虑混沌调制和混沌键控两种信息加密方法,采用先进的校正相减解调法恢复信息,并且通过解调Q因子衡量系统性能.以不同次序排列单模光纤和色散补偿光纤构成4种结构的光纤信道,通过数值仿真分析信道结构对系统通信性能的影响.结果表明,为光纤信道保留少量的负累积色散有利于改善远程传输性能;两端补偿和中间补偿这两种对称结构的性能一致,对称结构优于非对称结构即预补偿和后补偿,适用于远程高速开环全光混沌通信.另外,无论光纤信道结构怎样,在远程高速激光混沌通信中混沌调制方法总是优于混沌键控方法.     

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks(WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter(CKPF) is proposed in this paper.We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter(CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter(UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.     

Blind Extraction of Chaotic Signals by Using the Fast Independent Component Analysis Algorithm

We report the results of using the fast independent component analysis （FastICA） algorithm to realize Mind extraction of chaotic signals. Two cases are taken into consideration： namely, the mixture is noiseless or contaminated by noise. Pre-whitening is employed to reduce the effect of noise before using the FastICA algorithm. The correlation coefficient criterion is adopted to evaluate the performance, and the success rate is defined as a new criterion to indicate the performance with respect to noise or different mixing matrices. Simulation results show that the FastICA algorithm can extract the chaotic signals effectively. The impact of noise, the length of a signal frame, the number of sources and the number of observed mixtures on the performance is investigated in detail It is also shown that regarding a noise as an independent source is not always correct.