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 （FastlCA） method when noise contamination is considerable.

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

In this paper, a new method to reduce noises within chaotic signals based on ICA （independent component analysis） and EMD （empirical mode decomposition） is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the indepen- dent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions （IMFs） of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.

HARMONIC COMPONENT EXTRACTION FROM A CHAOTIC SIGNAL BASED ON EMPIRICAL MODE DECOMPOSITION METHOD

A novel approach of signal extraction of a harmonic component fRom a chaotic signal generated by a Duffing oscillator was proposed. Based on empirical mode decomposition （EMD） and concept that any signal is composed of a series of the simple intrinsic modes, the harmonic components were extracted f^om the chaotic signals. Simulation results show the approach is satisfactory.

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.

Chaotic signal denoising algorithm based on sparse decomposition

Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics.The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristics.We propose a denoising method of chaotic signals based on sparse decomposition and K-singular value decomposition(K-SVD)optimization.The observed signal is divided into segments and decomposed sparsely.The over-complete atomic library is constructed according to the differential equation of chaotic signals.The orthogonal matching pursuit algorithm is used to search the optimal matching atom.The atoms and coefficients are further processed to obtain the globally optimal atoms and coefficients by K-SVD.The simulation results show that the denoised signals have a higher signal to noise ratio and better preserve the chaotic characteristics.

PARTIALLY BLIND EXTRACTION OF CONTINUOUS CHAOTIC SIGNALS FROM A LINEAR MIXTURE

In this paper, we address the problem of blind extraction and separation of a continuous chaotic signal from a linear mixture consisting of some chaotic signal and/or random signals. The problem of blind extraction is firstly formulated as a problem of the synchronization-based parameter estimation. Then an efficient least square based parameter estimation method is introduced to determine the desired extracting vector. The proposed blind signal extraction scheme is applicable to blind separation of chaotic signals by formulating the separation problem as the extraction of each chaotic source. Numerical simulation shows that the proposed approach can blindly extract and separate the desired chaotic signals and it is also robust to measurement noise.

Study on Singularity of Chaotic Signal Based on Wavelet Transform

Based on the variations of wavelet transform modulus maxima at multi-scales, the singularity of chaotic signals are studied, and the singularity of these signals are measured by the Lipschitz exponent.In the meantime, a nonlinear method is proposed based on the higher order statistics, on the other aspect, which characterizes the higher order singular spectrum (HOSS) of chaotic signals. All computations are done with Lorenz attractor, Rossler attractor and EEG(electroencephalogram) time series and the comparisions among these results are made. The experimental results show that the Lipschitz exponents and the higher order singular spectra of these signals are significantly different from each other, which indicates these methods are effective for studing the singularity of chaotic signals.

Data signal processing via manchester coding-decoding method using chaotic signals generated by PANDA ring resonator

We investigate the nonlinear behaviors of light recognized as chaos during the propagation of Gaussian laser beam inside a nonlinear polarization maintaining and absorption reducing （PANDA） ring resonator system. It aims to generate the nonlinear behavior of light to obtain data in binary logic codes for transmission in fiber optics communication. Effective parameters, such as refractive indices of a silicon waveguide, coupling coefficients （~）, and ring radius ring （R）, can be properly selected to operate the nonlinear behavior. Therefore, the binary coded data generated by the PANDA ring resonator system can be decoded and converted to Manchester codes, where the decoding process of the transmitted codes occurs at the end of the transmission link. The simulation results show that the original codes can be recovered with a high security of signal transmission using the Manchester method.

Wavelet threshold method of resolving noise interference in periodic short-impulse signals chaotic detection

The chaotic oscillator has already been considered as a powerful method to detect weak signals, even weak signals accompanied with noises. However, many examples, analyses and simulations indicate that chaotic oscillator detection system cannot guarantee the immunity to noises （even white noise）. In fact the randomness of noises has a serious or even a destructive effect on the detection results in many cases. To solve this problem, we present a new detecting method based on wavelet threshold processing that can detect the chaotic weak signal accompanied with noise. All theoretical analyses and simulation experiments indicate that the new method reduces the noise interferences to detection significantly, thereby making the corresponding chaotic oscillator that detects the weak signals accompanied with noises more stable and reliable.

Continuous-time chaotic systems: Arbitrary full-state hybrid projective synchronization via a scalar signal

Relerrlng to contlnuous-Ume claaotlc systems, tills paper presents a new projective syncnromzatlon scheme, wnlcn enables each drive system state to be synchronized with a linear combination of response system states for any arbitrary scaling matrix. The proposed method, based on a structural condition related to the uncontrollable eigenvalues of the error system, can be applied to a wide class of continuous-time chaotic （hyperchaotic） systems and represents a general framework that includes any type of synchronization defined to date. An example involving a hyperchaotic oscillator is reported, with the aim of showing how a response system attractor is arbitrarily shaped using a scalar synchronizing signal only. Finally, it is shown that the recently introduced dislocated synchronization can be readily achieved using the conceived scheme.

动作时序优化振动信号混沌吸引子特征的断路器操动状态辨识方法

高压断路器分合闸操作过程包括掣子脱扣、弹簧释能、动静触头碰撞等动作,机械振动信号作为能量传递的表现,蕴含着丰富的运行状态信息。该文基于断路器关键动作节点的时序,提出一种动作时序优化振动信号混沌吸引子特征的断路器操动状态辨识方法。首先,由高速相机捕捉断路器操动过程主轴拐臂运动图像,利用目标跟踪算法获取动触头关键运动节点时序指标;然后,依据时序指标划分操动过程,通过对不同阶段的振动信号相空间重构,提取相空间吸引子几何和属性特征;最后,利用智能分类算法进行故障辨识,实验结果验证该方法在大幅节省时间开销的同时,有效提高了分类辨识的准确率达96.9%。该文方法在断路器状态监测和带电测试中具有广阔应用前景。

基于空时分组/混沌编码的多天线多基频通信编码研究

为了优化空时分组编码(STBC)和混沌编码,提升无线通信的性能与可靠性,采用多基频混沌编码(MFCC)结合空时分组编码技术搭建了一种双重编码模型,并对该模型的编码解码性能进行仿真分析,评估模型的编码解码性能。实验结果显示,当信噪比为10 dB时,新模型的误码率低至10-8,并且能在两基频条件下实现3548 b/s的高吞吐量。表明该编码解码模型不仅能降低误码率,还能有效提高基频的吞吐量,为促进无线通信技术的发展提供了有力支持。

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.

Health Monitoring of Long-Haul Fiber Communication System Using Chaotic OTDR

A novel chaotic optical time-domain reflectometry(OTDR)-based approach was proposed for monitoring long-haul fiber communication systems with multiple fiber segments. The self-phase modulation and group velocity dispersion effects of the optical cable was considered in demonstrating the proof-of-concept experiment and simulation. In experiments, the correlation peaks are clearly obtained from the correlation trace between the reference and reflected(or scattered) light signals propagating in three optical-fiber segments. The technique affords a high spatial resolution of 2 m, and further long-haul fiber simulations indicate that the sensing distance can be more than 3300 km. Thus, the new proposed technique can be effectively applied for health monitoring of long-haul fiber communication systems.

一种新的地磁信号去噪算法研究

地磁观测数据应用十分广泛。由地磁观测数据对各种环境因素的敏感性,严重影响了地磁数据的处理和利用。如果使用传统的噪声消除算法,难以获得良好的噪声消除效果。混沌序列可以提供一定的随机性和非线性特性,以帮助优化算法更好地搜索最优解。地磁信号则是需要去噪的原始信号。通过使用混沌蚁群算法构建目标函数,可以对地磁信号进行优化和去噪处理。算法会根据目标函数的设定,通过模拟蚁群行为和混沌序列的引导,逐步调整信号的参数和特征,以达到减小噪声、提取有效信息的目的。

基于改进Chebyshev混沌映射的抗比相法IFM测频信号设计

为了降低雷达发射信号的载频被瞬时测频(Instantaneous Frequency Measuring, IFM)截获的概率,基于抗IFM接收机信号的设计原理,提出了一种运用新型混沌映射产生的强随机、宽间隔频率捷变序列的方法,对线性调频连续波信号(Linear Frequency Modulated Continuous Wave, LFMCW)的载频进行调制,设计了一种抗IFM测频信号。仿真结果表明,所提混沌映射具有强随机性以及高复杂度,并且IFM接收机对所设计信号载频进行测量时,90.5%的测量结果与真实值的绝对误差大于200 MHz, 77.5%的测量结果与真实值的绝对误差大于500 MHz,表明所设计的信号具有较好的抗截获性能,抗IFM测频效果良好。

一种时间反转的参考信号调制DCSK通信方案

针对DCSK系统信息传输速率低及安全性差的问题,提出一种时间反转参考信号调制DCSK(TR-RMDCSK)通信系统方案。该方案采用RM调制实现了在一个帧时隙传输两个比特,携带偶数信息比特的混沌信号既是信息信号,也是传输奇数信息比特的参考信号,将参考信号进行时间反转,消除了同一帧时隙信号间的相关性,提升了比特传输速率及信号保密性。推导了高斯信道下的理论误码率,并在高斯信道和莱斯信道下进行了性能仿真分析。结果表明,该方案在不牺牲传输速率的情况下改善了误码性能,提高了系统安全性。

抗DRFM欺骗干扰的多路频率捷变LFMCW信号设计

在电子对抗领域中,数字射频存储(DRFM)干扰机能快速存储和再现射频信号,并对先进体制雷达进行有效干扰。首先对DRFM干扰机原理进行了分析,然后通过对传统Chebyshev混沌映射进行改进,设计了一种新型混沌系统,接着运用新型混沌系统对线性调频连续波(LFMCW)信号进行脉间调制,设计了一种抗DRFM欺骗干扰信号,最后对改进的混沌映射系统的随机性、复杂性和所设计信号的抗干扰性能进行了仿真分析验证。仿真结果表明,改进的混沌映射系统具有强随机性以及高复杂度,并且设计的信号具有较好的抗干扰性能,抗DRFM欺骗干扰效果良好。

基于神经网络的舰船混沌保密通信系统研究

以提升舰船通信安全性及同步性为出发点,设计基于神经网络的舰船混沌保密通信系统。基于舰船混沌同步保密通信系统一般模型,在系统发射端设计基于径向基函数神经网络(RBFNN)的跟踪器,跟踪舰船信息信号,通过混沌调制模块调制系统的状态量参数,并加密舰船信息信号将其混沌视为载波进行混沌掩盖后,通过信道进行传输,经噪声消除器去除信号噪声后,在混沌解调模块设计基于RBFNN的同步控制器,输出同步解调后的原始舰船信息信号传输至接收端,实现舰船信息保密同步通信。实验结果显示,该系统加密后,信息可有效掩盖信号真实情况,信号去噪后可还原信号本真,解调后可在接收端实现高精度的信号还原;且该系统应用后的信息可有效防止攻击者窃取。

Application of local polynomial estimation in suppressing strong chaotic noise

In this paper, we propose a new method that combines chaotic series phase space reconstruction and local polynomial estimation to solve the problem of suppressing strong chaotic noise. First, chaotic noise time series are reconstructed to obtain multivariate time series according to Takens delay embedding theorem. Then the chaotic noise is estimated accurately using local polynomial estimation method. After chaotic noise is separated from observation signal, we can get the estimation of the useful signal. This local polynomial estimation method can combine the advantages of local and global law. Finally, it makes the estimation more exactly and we can calculate the formula of mean square error theoretically. The simulation results show that the method is effective for the suppression of strong chaotic noise when the signal to interference ratio is low.