Low-Complexity Reconstruction of Covariance Matrix in Hybrid Uniform Circular Array

Spatial covariance matrix(SCM) is essential in many multi-antenna systems such as massive multiple-input multiple-output(MIMO). For multi-antenna systems operating at millimeter-wave bands, hybrid analog-digital structure has been widely adopted to reduce the cost of radio frequency chains.In this situation, signals received at the antennas are unavailable to the digital receiver, and as a consequence, traditional sample average approach cannot be used for SCM reconstruction in hybrid multi-antenna systems. To address this issue, beam sweeping algorithm(BSA) which can reconstruct the SCM effectively for a hybrid uniform linear array, has been proposed in our previous works. However, direct extension of BSA to a hybrid uniform circular array(UCA)will result in a huge computational burden. To this end, a low-complexity approach is proposed in this paper. By exploiting the symmetry features of SCM for the UCA, the number of unknowns can be reduced significantly and thus the complexity of reconstruction can be saved accordingly. Furthermore, an insightful analysis is also presented in this paper, showing that the reduction of the number of unknowns can also improve the accuracy of the reconstructed SCM. Simulation results are also shown to demonstrate the proposed approach.

基于CMT技术的非正侧面阵机载雷达杂波抑制方法研究

非正侧面阵机载雷达杂波谱与正侧面阵机载雷达杂波谱的主要区别在于它的杂波二维谱图随距离的变化而变化,杂波自由度相对增加,造成常规的统计型STAP杂波抑制方法不再适用.本文通过对非正侧面阵机载相控阵雷达杂波的分布进行详细的分析和研究,提出了一种基于CMT技术的非正侧面阵机载相控阵雷达杂波抑制方法.仿真结果表明了本文方法的有效性.

CMA-ES算法优化网络安全态势预测模型

针对网络安全态势预测问题,提出了一种预测方法。该方法采用协方差矩阵自适应进化策略(CMA-ES)算法来优化径向基神经网络(RBF)预测模型中的参数,使得RBF预测模型具备更好的泛化能力,可以快速的找出复杂时间序列中的规律。仿真实验结果表明,采用CMA-ES优化的RBF预测模型能够准确预测出一段时间内的网络安全态势值,预测精度高于传统预测手段。

基于CMAES集成学习方法的地表水质分类

为了提高人民生活质量,政府部门不断加强水质管理,然而人工分类方法无法满足实时处理的需求,传统机器学习方法的分类准确率又不够高。集成学习使用多种学习算法来获得比单一学习算法更好的预测性能。首先,对集成学习进行概述,简要介绍了Bagging和Boosting算法,并提出基于协方差自适应调整的进化策略算法(CMAES)的集成学习方法。接着,介绍了数据处理方式、模型评估方法和评价指标。最后,用CMAES集成学习方法对逻辑回归、线性判别分析、支持向量机、决策树、完全随机树、朴素贝叶斯、K-邻近算法、随机森林、完全随机树林、深度级联森林十种模型进行集成。实验结果表明,CMAES集成学习方法优于所有其他模型,该方法将继续被应用到未来的研究之中。

基于适应性均衡模型的CMAES约束优化算法

提出一种基于适应性均衡模型的协方差矩阵自适应进化策略(ATM-CMAES)用于求解约束优化问题。ATM-CMAES中的CMAES利用当前代最优子群与上一代分布均值之间的关系更新协方差矩阵来调整种群分布,将协方差矩阵秩1和秩μ2种更新机制相结合,克服了传统进化算法对种群大小的过分依赖;将ATM作为约束处理技术,针对群体不可行、半可行和可行这3种不同情形,采用不同的约束违反度与目标函数值之间的均衡机制来指导群体进化。对13个标准测试函数的实验结果表明:ATM-CMAES具有通用、稳健和高效性能。

基于CMAES算法的加速度计多位置新型标定方法

针对粒子群优化算法(PSO)和遗传算法(GA)在加速度计标定优化后期出现早熟、陷入局部最优,以及在设计与应用过程中存在的缺陷,将自适应协方差矩阵进化策略(CMAES)算法应用于加速度计的快速标定:采用具有不同函数特征的Sphere、Rastrigin和Rosen三个基准函数对比测试CMAES算法的总体性能;以模观测标定方法为基础建立加速度计标定模型,选取加速度计的24个位置进行仿真观测。实验结果表明:CMAES算法在收敛速度、收敛精度、全局搜索等方面性能优异,将加速度计的标定精度提升了12个数量级,为其它算法标定加速度计奠定了良好基础,对位移系统测量研究具有重要意义。

模糊云资源调度的CMA-PSO算法

针对多目标云资源调度问题,以优化任务的总完成时间和总执行成本为目标,采用模糊数学的方法,建立了模糊云资源调度模型。利用协方差矩阵能够解决非凸性问题的优势,采取协方差进化策略对种群进行初始化,并提出了一种混合智能优化算法CMA-PSO算法(covariance matrix adaptation evolution strategy particle swarm optimization,CMA-PSO),并使用该算法对模糊云资源调度模型进行求解。使用Cloudsim仿真平台随机生成云计算资源调度的数据,对CMA-PSO算法进行测试,实验结果证明了CMA-PSO算法对比PSO算法(particle wwarm optimization),在寻优能力方面提升28%,迭代次数相比提升20%,并且具有良好的负载均衡性能。

基于BMP及CMR的抗主瓣干扰算法研究

对于相控阵雷达抗主瓣干扰问题,本文通过对空时联合域抗主瓣干扰算法的研究,分析了基于阻塞矩阵预处理后常用的加权系数补偿法、白化法、对角加载法,以及对角加载结合线性约束波束保形算法存在的不足,提出了一种适用于采样快拍包含目标信号情况下的抗主瓣干扰算法。通过分析阻塞矩阵预处理后数据特征值的变化情况,修正预处理导致的过处理现象,从而重构协方差矩阵,该算法适用于阻塞矩阵预处理导致的自由度损失的情况,能够解决由于预处理导致的主瓣波峰偏移等失真问题,同时算法复杂度较低。该算法最大的优点是当采样快拍包含目标信号时,其抗干扰性能较好,快拍敏感度相比常规的波束保形方法更低,经实测数据验证,结果显示出该算法的优越性。

改进CMA-ES算法及其在7自由度仿人臂逆运动学求解中的应用

提出一种改进的CMA-ES算法:将原算法随机生成初始均值点,改为由佳点集中优秀个体加权求和得到;增加越界敏感因子和步长缩放系数,用于新个体存在越界行为时,修正步长更新。以7自由度仿人臂为例,用改进的CMA-ES算法求逆运动学解,结果表明改进的CMA-ES算法可实时、高精度地求解:在点对点运动中,改进的算法单次求解时间约为9.7 ms,适应度函数稳定在10^-8级别;在工作空间的连续轨迹中,位置跟踪误差稳定在10^-5 mm级别,单次平均求解时间约为14.1 ms。

融合AR模型和MCMC方法的水文模拟不确定性分析

为提高水文模型参数识别的可靠性,融合自回归模型与马尔可夫链-蒙特卡洛方法(auto regressive model based modified Markov Chain-Monte Carlo,AR-MCMC),利用自回归模型刻画残差序列的自相关性,修正MCMC方法中的残差协方差矩阵。通过新疆提孜那甫河流域融雪径流模型(SRM)的案例分析发现:融雪径流模拟的残差序列具有显著的自相关性;修正残差协方差矩阵后,边缘似然值更大;综合考虑多项评价指标,AR-MCMC方法在识别期与验证期推求的预测区间均优于MCMC方法;对比2种方法在识别期与验证期的纳什系数,采用AR-MCMC方法依次为0.86、0.89,而采用MCMC方法依次为0.84、0.87,即AR-MCMC方法获取的模型拟合效果更好。分析结果表明,相对于传统的MCMC方法,AR-MCMC方法能够更好地对研究区融雪径流过程进行模拟预测。

Geomagnetic jerk extraction based on the covariance matrix

We normalize data from 43 Chinese observatories and select data from ten Chinese observatories with most continuous records to assess the secular variations(SVs)and geomagnetic jerks by calculating the deviations between annual observed and CHAOS-6 model monthly means.The variations in the north,east,and vertical eigendirections are studied by using the covariance matrix of the residuals,and we find that the vertical direction is strongly affected by magnetospheric ring currents.To obtain noise-free data,we rely on the covariance matrix of the residuals to remove the noise contributions from the largest eigenvalue or vectors owing to ring currents.Finally,we compare the data from the ten Chinese observatories to seven European observatories.Clearly,the covariance matrix method can simulate the SVs of Dst,the jerk of the northward component in 2014 and that of the eastward component in 2003.5 in China are highly agree with that of Vertically downward component in Europe,compare to CHAOS-6,covariance matrix method can show more details of SVs.

Using position specific scoring matrix and auto covariance to predict protein subnuclear localization

The knowledge of subnuclear localization in eukaryotic cells is indispensable for under-standing the biological function of nucleus, genome regulation and drug discovery. In this study, a new feature representation was pro-posed by combining position specific scoring matrix (PSSM) and auto covariance (AC). The AC variables describe the neighboring effect between two amino acids, so that they incorpo-rate the sequence-order information;PSSM de-scribes the information of biological evolution of proteins. Based on this new descriptor, a support vector machine (SVM) classifier was built to predict subnuclear localization. To evaluate the power of our predictor, the benchmark dataset that contains 714 proteins localized in nine subnuclear compartments was utilized. The total jackknife cross validation ac-curacy of our method is 76.5%, that is higher than those of the Nuc-PLoc (67.4%), the OET- KNN (55.6%), AAC based SVM (48.9%) and ProtLoc (36.6%). The prediction software used in this article and the details of the SVM parameters are freely available at http://chemlab.scu.edu.cn/ predict_SubNL/index.htm and the dataset used in our study is from Shen and Chou’s work by downloading at http://chou.med.harvard.edu/ bioinf/Nuc-PLoc/Data.htm.

Fast and accurate covariance matrix reconstruction for adaptive beamforming using Gauss-Legendre quadrature

Most of the reconstruction-based robust adaptive beamforming(RAB)algorithms require the covariance matrix reconstruction(CMR)by high-complexity integral computation.A Gauss-Legendre quadrature(GLQ)method with the highest algebraic precision in the interpolation-type quadrature is proposed to reduce the complexity.The interference angular sector in RAB is regarded as the GLQ integral range,and the zeros of the threeorder Legendre orthogonal polynomial is selected as the GLQ nodes.Consequently,the CMR can be efficiently obtained by simple summation with respect to the three GLQ nodes without integral.The new method has significantly reduced the complexity as compared to most state-of-the-art reconstruction-based RAB techniques,and it is able to provide the similar performance close to the optimal.These advantages are verified by numerical simulations.

AN IMPROVED SAR-GMTI METHOD BASED ON EIGEN-DECOMPOSITION OF THE SAMPLE COVARIANCE MATRIX

An improved two-channel Synthetic Aperture Radar Ground Moving Target Indication (SAR-GMTI) method based on eigen-decomposition of the covariance matrix is investigated. Based on the joint Probability Density Function (PDF) of the Along-Track Interferometric (ATI) phase and the similarity between the two SAR complex images, a novel ellipse detector is presented and is applied to the indication of ground moving targets. We derive its statistics and analyze the performance of detection process in detail. Compared with the approach using the ATI phase, the ellipse detector has a better performance of detection in homogenous clutter. Numerical experiments on simulated data are presented to validate the improved performance of the ellipse detector with respect to the ATI phase approach. Finally, the detection capability of the proposed method is demonstrated by measured SAR data.

Coupled Cross-correlation Neural Network Algorithm for Principal Singular Triplet Extraction of a Cross-covariance Matrix

This paper proposes a novel coupled neural network learning algorithm to extract the principal singular triplet (PST) of a cross-correlation matrix between two high-dimensional data streams. We firstly introduce a novel information criterion (NIC), in which the stationary points are singular triplet of the crosscorrelation matrix. Then, based on Newton's method, we obtain a coupled system of ordinary differential equations (ODEs) from the NIC. The ODEs have the same equilibria as the gradient of NIC, however, only the first PST of the system is stable (which is also the desired solution), and all others are (unstable) saddle points. Based on the system, we finally obtain a fast and stable algorithm for PST extraction. The proposed algorithm can solve the speed-stability problem that plagues most noncoupled learning rules. Moreover, the proposed algorithm can also be used to extract multiple PSTs effectively by using sequential method. © 2014 Chinese Association of Automation.

高斯过程下的CMA-ES在医学图像配准中的应用

为了改进协方差矩阵自适应进化策略(CMA-ES)的性能,提出了一种高斯过程协助下的协方差矩阵自适应进化策略(GPACMA-ES)。该策略利用CMA-ES中的协方差矩阵构建核函数,引入高斯过程,在线学习历史经验,并根据历史经验预测全局最优解的最有前景区域,有效地降低了适应度函数的评价次数。同时,为了提高群体的搜索效率,引入了置信区间。群体在置信区间内更高效地采样,使得算法具备更快的收敛速度和全局寻优能力。最后,将GPACMA-ES算法应用于医学图像配准中,配准精度和效率均高于标准的CMA-ES算法。

IMPROVED ESTIMATES OF THE COVARIANCE MATRIX IN GENERAL LINEAR MIXED MODELS

In this article, the problem of estimating the covariance matrix in general linear mixed models is considered. Two new classes of estimators obtained by shrinking the eigenvalues towards the origin and the arithmetic mean, respectively, are proposed. It is shown that these new estimators dominate the unbiased estimator under the squared error loss function. Finally, some simulation results to compare the performance of the proposed estimators with that of the unbiased estimator are reported. The simulation results indicate that these new shrinkage estimators provide a substantial improvement in risk under most situations.

Two Stage Estimation and Its Covariance Matrix in Multivariate Seemingly Unrelated Regression System

Multivariate seemingly unrelated regression system is raised first and the two stage estimation and its covariance matrix are given. The results of the literatures[1-5] are extended in this paper.

Covariance Matrix Learning Differential Evolution Algorithm Based on Correlation

Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which makes the search move in a more favorable direction. In order to obtain more accurate information about the function shape, this paper proposes covariance matrix learning differential evolution algorithm based on correlation (denoted as RCLDE) to improve the search efficiency of the algorithm. First, a hybrid mutation strategy is designed to balance the diversity and convergence of the population;secondly, the covariance learning matrix is constructed by selecting the individual with the less correlation;then, a comprehensive learning mechanism is comprehensively designed by two covariance matrix learning mechanisms based on the principle of probability. Finally, the algorithm is tested on the CEC2005, and the experimental results are compared with other effective differential evolution algorithms. The experimental results show that the algorithm proposed in this paper is an effective algorithm.

Empirical Likelihood Statistical Inference for Compound Poisson Vector Processes under Infinite Covariance Matrix

The paper discusses the statistical inference problem of the compound Poisson vector process(CPVP)in the domain of attraction of normal law but with infinite covariance matrix.The empirical likelihood(EL)method to construct confidence regions for the mean vector has been proposed.It is a generalization from the finite second-order moments to the infinite second-order moments in the domain of attraction of normal law.The log-empirical likelihood ratio statistic for the average number of the CPVP converges to F distribution in distribution when the population is in the domain of attraction of normal law but has infinite covariance matrix.Some simulation results are proposed to illustrate the method of the paper.