弹道导弹进动周期雷达测量提取方法研究

主要研究了利用雷达目标RCS序列提取弹道导弹进动周期的方法.首先建立了弹道导弹中段目标的进动模型,分析了目标RCS回波数据与进动周期的关系表达式,然后给出了利用目标RCS序列估计进动周期的方法.仿真实验结果表明,文中提出的方法能够有效估计目标的进动周期.     

应用RCS序列估计卫星自旋周期

针对空间探测任务中采用雷达散射截面积(RCS)序列估计卫星旋转周期存在的问题,建立了基于多频段RCS的卫星自旋周期估计分析模型.根据卫星的外推弹道,计算了卫星的可跟踪弧段,推导了自旋模式下卫星本体坐标系下电磁波入射角的计算公式.采用电磁场数值算法快速计算卫星的RCS,通过RCS匹配获得卫星可跟踪弧段的理论RCS序列,研究了自旋周期在RCS序列中的表现形式.仿真分析了雷达频段、采样率及弧段选择对周期估计的影响,结果表明入射角序列相对于垂直于卫星自旋轴方向变化平稳的弧段,RCS序列呈现的周期性特征显著,利用该类弧段进行卫星自旋周期估计可以得到准确的结果,证明该方法可以应用于卫星自旋周期估计.     

基于窄带RCS数据的低速旋转空间目标识别研究北大核心CSCD

空间运动目标RCS数据序列能反映出空间目标的姿态运动特征。针对空间运动目标RCS数据序列的变化规律,首先仿真生成低速旋转空间目标的RCS数据序列,而后采用小波变换、傅里叶变换以及RCS数据序列统计学特征提取等方法,对低速旋转空间目标的RCS数据序列进行特征提取。最后采用朴素贝叶斯、支持向量机、随机森林分类和logistic逻辑回归算法等机器学习分类算法,实现了对低速旋转空间目标RCS数据序列的识别。     

基于深度神经网络模型的雷达目标识别

根据雷达测量的目标电磁散射面积(RCS)序列,采用深度神经网络模型识别空间飞行目标。首先,阐述了提取RCS时间序列特征的方法,包括均值、均方差及周期特性等特征;然后,给出了深度神经网络模型识别RCS目标的算法;最后,用仿真数据验证该识别方法,数值实验结果表明该方法能较准确识别雷达跟踪目标。     

基于ARIMA-RNN组合模型的云服务器老化预测方法

针对云服务器系统运行环境具有非线性、随机性和突发性的特点,提出了基于整合移动平均自回归和循环神经网络组合模型(ARIMA-RNN)的软件老化预测方法.首先,采用ARIMA模型对云服务器时间序列数据进行老化预测;然后,利用灰色关联度分析法计算时间序列数据的相关性,确定RNN模型的输入维度;最后,将ARIMA模型预测值和历...     

基于三角函数拟合的RCS序列进动周期估计

利用雷达反射截面(RCS)序列估计进动周期为弹道目标特征提取和识别的重要途径。弹道目标在进动时,回波RCS序列为非平稳的周期序列,常规Fourier变换方法和周期间相关类方法需要较长观测时间和较高数据率才能有效地估计RCS的周期,这对于有限的雷达资源来说是不可接受的。该文提出一种新的估计弹道目标RCS序列周期的方法,该方法先利用特定频率附近的三角函数来拟合RCS序列,再求得使拟合误差最小的频率,即为RCS序列的进动频率。相比于常规方法,该文方法具有所需资源少,估计精度高的特点。RCS计算数据的仿真结果证明了该文方法的有效性。     

基于隐马尔科夫模型的RCS识别方法研究

雷达散射截面(RCS)时间序列由目标电磁散射特性和姿态运动特性共同决定,包含了雷达目标的材质、尺寸和结构等信息,是实现雷达目标识别的重要测量量.隐马尔科夫模型(HMM)是一种用参数表示的用于描述随机过程统计特性的概率模型,是一个无记忆的非平稳随机过程,具有很强的表征时变信号的能力,非常适合作为动态模式分类器,对具有不同变化特性的时变信号进行分类识别.文中利用HMM表征雷达目标RCS序列变化模式(规律),根据不同类别目标RCS序列变化模式的差异对雷达目标进行分类识别.实测数据验证结果表明,该算法具有较高的识别概率.     

一种雷达散射截面半参数起伏统计模型

提出一种基于支持向量回归机(Support Vector Regression,SVR)的半参数化雷达散射截面(Radar Cross Section,RCS)起伏统计模型。该模型通过利用 SVR 将常规半参数化模型中修正因子全样本表出简化为支持向量表出,从而达到提高模型执行效率的目的。仿真实验结果表明,该模型可以有效表达 RCS 样本分布,且显著降低模型表出所需样本量。     

一种基于加权隐马尔可夫的 自回归状态预测模型

针对电子系统状态趋势预测问题,提出了一种加权隐马尔可夫模型的自回归趋势预测方法.该方法以自回归模型作为隐马尔可夫的状态输出,利用加权预测思想对马尔可夫链中的隐状态进行混合高斯模型的加权序列预测,并利用最大概率隐状态下的自回归系数计算模型输出.通过对实际的复杂混沌序列和电子系统BIT状态数据进行趋势预测,并针对不同模型参数下的预测结果进行实验分析,结果表明该方法对系统状态变化的趋势具有较好的预测性能.     

SAR导引头中制导地面场景RCS计算

针对SAR导引头中段匹配制导中地面场景RCS的计算,综合了现有利用数字高程图(DEM)的SAR图像模拟方法,归纳了SAR导引头中制导地面场景RCS系统的计算步骤和实现方法.该方法首先得到数字高程图数据,再利用分形插值和小平面单元模型对数字高程图数据进行处理,然后结合RCS经验模型计算出地面场景的RCS.该方法步骤详尽、流程清晰,直接适合计算机仿真.针对文中归纳的RCS系统的实现方法,结合地面场景RCS仿真流程,进行了仿真实验,实验结果验证了该方法的有效性,实用性.进而为SAR导引头的目标回波模拟提供了一定的理论依据.     

一种基于频繁模式的时间序列分类框架

如何提取和选择时间序列的特征是时间序列分类领域两个重要的问题。该文提出MNOE(Mining Non- Overlap Episode)算法计算时间序列中的非重叠频繁模式,并将其作为时间序列特征。基于这些非重叠频繁模式,该文提出EGMAMC(Episode Generated Mixed memory Aggregation Markov Chain)模型描述时间序列。根据似然比检验原理,从理论上推导出频繁模式在时间序列中出现的次数和EGMAMC模型是否能显著描述时间序列之间的关系;根据信息增益定义,选择能显著描述时间序列的频繁模式作为时间序列特征输入分类模型。在UCI (University of California Irvine)公共数据集和实际智能楼宇数据集上的实验表明,选择频繁模式作为特征进行分类的准确率、召回率和F-Measure均优于不选择频繁模式作为特征的分类结果。高效的计算和有效的选择非重叠频繁模式作为时间序列特征有助于提高时间序列分类模型的各项评价指标。     

基于非线性约束的局部投影降噪

基于相空间重构理论,该文提出了一种改进的混沌时序降噪方法.首先利用递归图对实际观测的时间序列进行混沌特性分析,然后将非线性约束条件引入局部投影方法之中,并在局部邻域内进行奇异谱(SSA)分析,利用代表吸引子的主分量来重构时间序列.该算法克服了传统局部投影方法不能充分刻画系统内在非线性关系的问题,减小了重构误差,提高了系统的信噪比.通过对Lorenz模型和太阳黑子混沌时间序列进行仿真分析,证实了该文算法对实际观测混沌时序降噪的有效性.     

The higher order theory of generalized almost-cyclostationary timeseries

In this paper, the class of generalized almost-cyclostationary (GACS) time series is introduced. Time series belonging to this class are characterized by multivariate statistical functions that are almost-periodic functions of time whose Fourier series expansions can exhibit coefficients and frequencies depending on the lag shifts of the time series. Moreover, the union over all the lag shifts of the lag-dependent frequency sets is not necessarily countable. Almost-cyclostationary (ACS) time series turn out to be the subclass of GACS time series for which the frequencies do not depend on the lag shifts and the union of the above-mentioned sets is countable. The higher order characterization of GACS time series in the strict and wide sense is provided. It is shown that the characterization in terms of cyclic moment and cumulant functions is inadequate for those GACS time series that are not ACS. Then, generalized cyclic moment and cumulant functions (in both the time and frequency domains) are introduced. Finally, the problem of estimating the introduced generalized cyclic statistics is addressed, and two examples of GACS time series are considered     

Multirate processing of time series exhibiting higher ordercyclostationarity

The paper investigates the effects of multirate systems on the higher order wide-sense cyclostationarity (WSCS) properties of discrete-time series. To this end, definitions and properties of the cyclic higher order statistics, which were introduced for continuous-time series in the fraction-of-time probability framework, are first extended to discrete-time series. Then, starting from the consideration of the basic multirate building blocks, viz., M-fold decimators and L-fold interpolators, results for typical interconnections are derived. The problem of eliminating the images in the cyclic higher order spectra of an interpolated time series is addressed. Moreover, the problem of avoiding aliasing (in both cycle and spectral frequency domains) in the cyclic higher order spectra of a decimated time series is considered. Finally, a sufficient condition to avoid both aliasing and imaging effects in the cyclic higher order spectra of a time series decimated by a fractional factor is derived     

一种面向对象的时间序列极化SAR图像变化检测方法

提出了一种改进的时间序列极化合成孔径雷达(SAR)图像变化检测方法。该方法首先使用简单线性迭代聚类算法对时间序列极化SAR图像进行过分割,得到时间序列图像的超像素一致表达。然后对各个超像素进行时间序列上的任意双时相交叉变化检测,得到包含时间序列图像变化信息的变化检测矩阵。最后根据变化信息对图像在时间序列上进行平滑滤波并生成动态变化图,对地物变化情况进行分析。实验结果表明,文中方法能够有效地检测和分析时间序列极化SAR图像的变化情况。     

正则化训练的神经网络与粗集理论相结合的股票时间序列数据挖掘技术

论文提出将正则化神经网络与粗集理论相结合应用于股票时间序列数据库的数据挖掘.首先对时间序列数据库进行预处理,除去高频干扰信号,然后将股票时间序列数据按照收盘价的变化趋势分割成一系列静态模式,每种模式代表股票价格的一种行为趋势(上涨或下跌),把决定各种模式的相关属性组成一系列信息,形成一个适用于粗集方法的信息表.然后使用正则神经网络对信息表进行学习,用粗集理论从正则神经网络所存储的知识中抽取规则,得到的规则可以用于预测时间序列在未来的行为。该方法融合了正则神经网络优良的泛化性能和粗集理论的规则生成能力,实验表明,该方法预测效果比较准确。     

混沌时间序列的遗传演化建模

从实测时间序列中构造混沌系统的模型是非线性时间序列分析中的一个重要议题,本文利用遗传编程方法(GP),在尽可能大的函数空间内搜索合适的模型结构,并引入粒子群算法(PSO)在动态模型结构情况下进行非线性参数估计,取得了较好效果.此外,演化建模的实现结合了非线性时间序列分析(NTSA)的结果,以NTSA的结果指导演化建模参数的选取并作为模型优劣的评判标准,改进了经典GP算法对混沌系统建模的应用效果.     

Analysis of event-related fMRI data using best clustering bases

We explore a new paradigm for the analysis of event-related functional magnetic resonance images (fMRI) of brain activity. We regard the fMRI data as a very large set of time series x(i) (t), indexed by the position i of a voxel inside the brain. The decision that a voxel i(o) is activated is based not solely on the value of the fMRI signal at i(o), but rather on the comparison of all time series x(i) (t) in a small neighborhood Wi(o) around i(o). We construct basis functions on which the projection of the fMRI data reveals the organization of the time series x(i) (t) into activated and nonactivated clusters. These clustering basis functions are selected from large libraries of wavelet packets according to their ability to separate the fMRI time series into the activated cluster and a nonactivated cluster. This principle exploits the intrinsic spatial correlation that is present in the data. The construction of the clustering basis functions described in this paper is applicable to a large category of problems where time series are indexed by a spatial variable.     

Time Series Segmentation Using a Novel Adaptive Eigendecomposition Algorithm

In this paper we present a new technique for time series segmentation built around a fast principal component analysis (PCA) algorithm that is on-line and stable. The traditional Generalized Likelihood Ratio Test (GLRT) has been used to solve the segmentation problem, but this has enormous limitations in terms of complexity and speed. Newer methods use gated experts and mixture models to detect transitions in time series. These techniques perform better than GLRT, but most of them require extensive training of relatively large neural networks. The segmentation method discussed in this paper is based on a novel idea that involves solving the generalized eigendecomposition of two consecutive windowed time series and can be formulated as a two-step PCA. Thus, the performance of our segmentation technique mainly depends on the efficiency of the PCA algorithm. Most of the existing techniques for PCA are based on gradient search procedures that are slow and they also suffer from convergence problems. The PCA algorithm presented in this paper is both online, and is proven to converge faster than the current methods.     

State-dependent vector hybrid linear and nonlinear ARMA modeling: Applications

In a recent companion paper, a new method has been presented for modeling general vector nonstationary and nonlinear processes based on a state-dependent vector hybrid linear and nonlinear autoregressive moving average (SVH-ARMA) model. This paper discusses some potential applications of the SVH-ARMA model, including signal filtering, time series prediction, and system control. First, a state-space model governed by a hidden Markov Chain is shown to be equivalent to the SVH-ARMA model. Based on this state-space model, the extended Kalman filtering and Bayesian estimation techniques are applied for noisy signal enhancement. The result of a noisy image enhancement verifies that the model can track the time-varying statistical characteristics of nonstationary and nonlinear processes adaptively. Second, the SVH-ARMA model is used for a vector time series prediction, which can attain more accurate multiple step ahead prediction, than conventional forecasting methods. Third, a new technique is developed for predicting scalar long correlation time series in the wavelet scale space domain based on the SVH-ARMA model. Dyadic wavelet transform is employed to convert a scalar time series to a vector time series, to which the SVH-ARMA model is applied for vector time series prediction. More accurate and robust forecasting results in both one step and multiple step ahead prediction can be gained. See also the companion paper on theory, by Zheng et al., pp. 551–574, this issue.