跳频预测干扰之跟踪模型的改进

在跳频通信的研究中,针对实现实时跳频跟踪干扰问题,跳频预测干扰是通信电子战中跳频干扰中一项有效的技术.传统的跳频预测干扰的跟踪模型是跳频码序列预测.跳频码序列预测技术在处理实际的跳频通信码序列时实时性差,命中率并不高.为解决上述问题,提出了改进的跟踪模型,跳频同步频率集预测.同时还建立了更客观的新的预测性能评价标准模型.进行仿真的结果表明,与跳频码序列预测相比,跳频同步频率集预测有更高的实时性,使跳频预测提高了跳频跟踪干扰的效率.     

混沌跳频通信系统的设计与仿真

跳频通信系统的关键技术是跳频序列的同步。本文利用混沌信号Logistic序列作跳频码,取代跳频通信中的伪随机序列,并采用动态双频同步方案使同步头信号同步,解决了混沌跳频序列的同步难题,进而实现发射系统和接收系统的混沌同步。在Matlab7.0/Simulink动态仿真平台,搭建了一个基于该同步方案的跳频通信系统,仿真结果表明该同步方案用于跳频通信可获得满意的通信效果。     

基于混沌的跳频通信系统

利用混沌信号取代跳频通信中的伪随机序列,其关键问题是混沌序列的相关特性和发射系统接收系统的混沌同步。该文提出利用Chen’s混沌序列构造跳频码,分析了该混沌序列的相关特性,并与Lorenz混沌序列进行比较,并讨论了存在于扰下的同步性能。应用MATLAAB进行仿真,结果表明该混沌序列具有良好的相关特性和稳定的同步性能,应用于跳频通信可获得较好的效果。     

一种用MCS-51单片机实现的混沌跳频序列产生器

混沌作为一种非线性动态系统中的现象正在受到人们的极大重视，混沌理论的发展和不断成熟给跳频码序列的研究带来了新的希望。文中主要介绍混沌跳频序列的产生原理和一种以ＭＣＳ－５１单片机为核心配以其它辅助电路实现的混沌跳频序列产生器。     

基于混沌特性的跳频序列复杂度分析

对于已有的跳频序列复杂度分析方法,通过分析跳频序列的混沌特性,提出了一种用关联维数度量跳频序列复杂度的方法,该方法能够对满足混沌特性的跳频序列进行复杂度分析,为跳频序列复杂度的度量提供了一种新的参考依据。最后对基于L-G非连续抽头模型和Logistic-Kent级联映射构造的跳频序列进行了基于混沌特性的复杂度分析,并对两者的复杂度进行了比较。     

基于混沌序列的水声差分跳频G函数仿真

基于保密通信、组网通信的考虑,提出将差分跳频技术应用于水声通信并给出了系统模型,可以克服传统跳频通信的数据率不高和同步困难等不足.从CHESS电台G函数产生的跳频图案的缺点出发,利用混沌序列构建出新的G函数,将混沌序列按照一定的方法分割,冉将数据信息调制在随机序列上用以产生跳频号,对新旧G函数产生的跳频图案进行了性能检验和仿真分析.结果表明,新的G函数产生的跳频序列有良好的均匀性、随机性,应用于水下通信可以更好的抗水下干扰和多途,实现保密通信和组网要求.     

基于混沌序列的超宽带通信系统性能比较

本文采用混沌序列作为跳时超宽带（TH-UWB）系统和直接序列超宽带（DS-UWB）系统的跳时码和扩频码,针对混沌跳时脉冲位置调制、混沌跳时脉冲幅度调制和混沌直接序列脉冲幅度调制在加性高斯白噪声干扰下的误码率性能进行了分析。并用Matlab对性能进行了仿真,将仿真结果与理论分析结果进行比较,得出一些有益的结论。     

混沌理论在混沌跳频序列分析中的应用研究

传统分析跳频序列的方法仅从跳频特性方面考虑,此处应用混沌性判别理论研究了混沌跳频序列的混沌性,继而利用相空间重构理论对其分析了时间延迟和嵌入维数。利用相空间重构理论,可重构得到与原系统等价的系统,进而可指导分析原系统的特性。     

基于贝叶斯网络的跳频序列多步预测*

根据跳频频率序列具有混沌特性,在相空间重构理论基础上提出一种用于跳频频率序列预测的贝叶斯网络模型。该模型将重构后的整个相空间作为先验数据信息,进而通过学习贝叶斯网络并利用贝叶斯网络推理算法达到对跳频频率多步预测的目的。仿真结果表明该方法具有良好的多步预测能力,并能有效地克服过拟合现象。     

非重复宽间隔跳频序列的优化

跳频序列发生器是跳频通信系统的核心部件,寻求理想的跳频序列是研究跳频通信的重要课题之一.通过对RS码跳频序列相关技术的研究,改进了基于RS码宽间隔非重复跳频序列的优化方法,利用Matlab软件进行了仿真,并绘制出了某个跳频序列的汉明相关性.计算结果表明,该方法克服了对偶频带法产生的宽间隔的跳频序列随机性较差的缺点,提高了整个跳频序列的平均间隔,满足相邻频率值大于某个固定的常数,同时也满足宽间隔非重复跳频序列族的理论界限.     

Multi channel frequency hopping spread spectrum signaling using code M-ary frequency shift keying

In this paper we have proposed code M-ary frequency shift keying technique based frequency hopping (CMFSK/FH) spread spectrum signaling for multi channel. In this technique, taking one bit from each channel, we have constructed data word (called as symbol) for CMFSK system and corresponding to each state of data word, the transmitter generates a frequency, which is selected and amplified for transmission. The received signal in the receiver is tuned to each transmitted frequency and decoded for separation of channels. Here, we have developed simulation model for bit error rate (BER) performance analysis of CMFSK/FH system and compared the same with analytical results of BER. It is seen that simulation model is matched closely with analytical result. The BER performance for CMFSK/FH is better than that of existing FH system. It is also seen that the BER decreases with increase of frequency hop size.     

An integrated chaotic time series prediction model based on efficient extreme learning machine and differential evolution

In this paper, an integrated model based on efficient extreme learning machine (EELM) and differential evolution (DE) is proposed to predict chaotic time series. In the proposed model, a novel learning algorithm called EELM is presented and used to model the chaotic time series. The EELM inherits the basic idea of extreme learning machine (ELM) in training single hidden layer feedforward networks, but replaces the commonly used singular value decomposition with a reduced complete orthogonal decomposition to calculate the output weights, which can achieve a much faster learning speed than ELM. Moreover, in order to obtain a more accurate and more stable prediction performance for chaotic time series prediction, this model abandons the traditional two-stage modeling approach and adopts an integrated parameter selection strategy which employs a modified DE algorithm to optimize the phase space reconstruction parameters of chaotic time series and the model parameter of EELM simultaneously based on a hybrid validation criterion. Experimental results show that the proposed integrated prediction model can not only provide stable prediction performances with high efficiency but also achieve much more accurate prediction results than its counterparts for chaotic time series prediction.     

基于最大互信息的混沌时间序列多步预测

针对混沌时间序列的多步预测,提出了基于最大互信息（MMI）的建模方法.首先建立时间延迟、嵌入维数和预测步长在相空间的最大信息量模型;然后利用遗传算法求解并确定混沌时间序列的最佳预测结构;最后对Mackey-Glass系统和月太阳黑子的仿真实验表明,MMI可以确定更好的预测结构,提高了混沌时间序列的预测精度.     

基于径向基神经网络的局域预测法及其应用

一般的加权一阶局域预测法是利用最小二乘法求解模型,从而对混沌时序进行预测。基于径向基神经网络的局域预测法是在加权一阶局域预测模型的理论基础上,应用径向基神经网络代替加权一阶局域预测模型构成了基于径向基神经网络的局域预测模型。通过对Logistic映射以及Lorenz系统的3个分量的混沌时间序列的预测仿真,表明该预测方法对混沌时间序列的预测具有较好的效果。     

交通流量VNNTF神经网络模型多步预测研究

研究了VNNTF 神经网络（Volterra neural network trafficflow model,VNNTF） 交通流量混沌时间序列多步预测问题. 通过分析比较交通流量混沌时间序列相空间重构的嵌入维数和Volterra 离散模型之间的关系,给出了确定交通流量Volterra 级数模型截断阶数和截断项数的方法,并在此基础上建立了VNNTF 神经网络交通流量时间序列模型;设计了交通流量Volterra 神经网络的快速学习算法;最后,利用交通流量混沌时间序列对VNNTF 网络模型,Volterra 预测滤波器和BP 网络进行了多步预测实验,比较了多步预测结果的仿真图、绝对误差的柱状图以及归一化后的方均根;实验结果表明VNNTF 神经网络的多步预测性能明显优于Volterra 预测滤波器和BP 神经网络.     

A chaotic time series prediction model for speech signal encoding based on genetic programming

In this paper, a novel solving method for speech signal chaotic time series prediction model was proposed. A phase space was reconstructed based on speech signal's chaotic characteristics and the genetic programming (GP) algorithm was introduced for solving the speech chaotic time series prediction models on the phase space with the embedding dimension m and time delay τ. And then, the speech signal's chaotic time series models were built. By standardized processing of these models and optimizing parameters, a speech signal's coding model of chaotic time series with certain generalization ability was obtained. At last, the experimental results showed that the proposed method can get the speech signal chaotic time series prediction models much more effectively, and had a better coding accuracy than linear predictive coding (LPC) algorithms and neural network model.     

基于支持向量机的混沌时间序列预测分析

混沌的特性决定了混沌系统很难长期预测,支持向量机有强大的学习能力,根据相空间重构理论用支持向量机建立预测模型对混沌时间序列进行短期预测。预测输出构建混沌吸引子来定性评价预测模型性能,同时与BP神经网络RBF神经网络构建的预测模型比较,计算预测模型的均方根误差定量地评价模型的性能。仿真结果表明,该方法具有较高的预测精度和泛化能力。     

遗传算法在混沌时间序列预测中的应用研究*

在混沌时间序列研究中,相空间重构和预测模型参数优化是影响预测性能的关键步骤,利用两者之间的相互联系来提高混沌时间序列预测模型的整体性能,提出一种基于遗传算法的混沌时间序列参数同步优化方法。同步优化方法将相空间重构和最小二乘支持向量机参数作为遗传算法的染色体,预测精度作为遗传算法的适应度函数值,通过遗传算法对参数同步优化问题进行求解。通过混沌时间数据对同步优化方法进行了验证性实验。实验结果表明,相对于单独参数优化方法,同步优化方法不仅提高了混沌时间序列的预测精度,同时降低了计算时间的复杂度。     

Prediction of multivariate chaotic time series with local polynomial fitting

To improve the prediction accuracy of complex multivariate chaotic time series, a novel scheme formed on the basis of multivariate local polynomial fitting with the optimal kernel function is proposed. According to Takens Theorem, a chaotic time series is reconstructed into vector data, multivariate local polynomial regression is used to fit the predicted complex chaotic system, then the regression model parameters with the least squares method based on embedding dimensions are estimated,and the prediction value is calculated. To evaluate the results, the proposed multivariate chaotic time series predictor based on multivariate local polynomial model is compared with a univariate predictor with the same numerical data. The simulation results obtained by the Lorenz system show that the prediction mean squares error of the multivariate predictor is much smaller than the univariate one, and is much better than the existing three methods. Even if the last half of the training data are used in the multivariate predictor, the prediction mean squares error is smaller than that of the univariate predictor.