Data-Based Filters for Non-Gaussian Dynamic Systems With Unknown Output Noise Covariance

This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

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.

Optimal Estimation of High-Dimensional Covariance Matrices with Missing and Noisy Data

The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based on complete data. This paper studies the optimal estimation of high-dimensional covariance matrices based on missing and noisy sample under the norm. First, the model with sub-Gaussian additive noise is presented. The generalized sample covariance is then modified to define a hard thresholding estimator , and the minimax upper bound is derived. After that, the minimax lower bound is derived, and it is concluded that the estimator presented in this article is rate-optimal. Finally, numerical simulation analysis is performed. The result shows that for missing samples with sub-Gaussian noise, if the true covariance matrix is sparse, the hard thresholding estimator outperforms the traditional estimate method.

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.

Evaluation of Multiplicative Weight of Covariance Matrix on Hybrid Data Assimilation Schemes

This research develops a comparative study between different multiplicative weights that are assigned to the covariance matrix that represents the background error in two hybrid assimilation schemes: 3DEnVAR and 4DEnVAR. These weights are distributed between the static and time-invariant matrix and the matrix generated from the perturbations of a previous ensemble. The assigned values are 25%, 50%, and 75%, always having as a reference the ensemble matrix. The experiments are applied to the short-range Prediction System (SisPI) that works operationally at the Institute of Meteorology. The impact of Tropical Storm Eta on November 7 and 8, 2020 was selected as a study case. The results suggest that by giving the main weight to the ensemble matrix more realistic solutions are achieved because it shows a better representation of the synoptic flow. On the other hand, it is observed that 3DEnVAR method is more sensitive to multiplicative weight change of the first guess. More realistic results are obtained with 50% and 75% relations with 4DEnVAR method, whereas with 3DEnVAR a weight of 75% for the ensemble matrix is required.

基于随机抽样的衰变热不确定度量化研究

为研究核数据引起的核素存量及导出量的不确定度,在自主程序GNET上实现了基于随机抽样的不确定度量化方法。利用贝叶斯更新方法获得裂变产物独立产额的协方差数据,弥补裂变产额协方差数据缺失。对热中子引起的235U一次裂变后衰变热不确定度进行了计算。结果表明,裂变产物产额的不确定度贡献占主要部分。该算例表明GNET程序具备了核素存量的不确定度量化功能。

2004-2010年上海崇明东滩湿地生态系统CO_(2)通量数据集

作为微气象学的经典方法之一,涡度相关技术已成为观测陆地生态系统与大气间CO_(2)、水热通量交换的主要手段。本数据集为上海长江河口湿地生态系统国家野外科学观测研究站在上海崇明东滩湿地于2004年9月至2010年12月期间观测累积的通量数据,时间分辨率为半小时,数据项包括气温(T_air)、相对湿度(RH)、土温(T_soil)、光合有效辐射(PAR)、总辐射(Solar)、净辐射(Rn)、降雨量(Precip)、感热通量(Hs)、潜热通量(LE)和二氧化碳净交换量(NEECO_(2))等。本研究观测的生态系统为典型亚热带滨海盐沼类型,优势植物为芦苇和互花米草。本数据集的观测系统架设、设备维护和数据质量控制及处理等均按照FLUXNET规范,数据可靠性高,可为中国滨海盐沼的碳收支、蓝碳估算及生物入侵影响评估等相关研究提供扎实的数据支持。

我国东部山地针阔混交林碳通量特征分析

以浙江省金华市武义县大毛尖山为研究区域,开展复杂山地森林生态系统碳汇能力的观测研究,并利用涡动相关法在2022年6月~2023年5月进行观测,经过数据质量控制和质量评价分析,得到42%的优质CO_(2)通量数据.结果表明,大毛尖山周边以针阔混交林为主,能量闭合度为0.89,能够很好的代表站点通量情况.CO_(2)通量在日尺度上均表现为U型变化,范围为-1.20~0.89mgCO_(2)/(m^(2)·s).四季碳汇能力强弱依次为,夏季、春季、秋季、冬季;各月份净生态系统碳交换量(NEE)均为负值,整体表现为碳汇.CO_(2)通量与气象因子中的空气温度呈负相关,相对湿度和平均风速正相关,夜间因呼吸作用产生的CO_(2)通量与土壤温度正相关.本研究初步解释了大毛尖山森林生态系统的碳汇特征.

青藏高原东南缘不同类型生态系统碳、水交换特征

青藏高原东南缘横断山脉地区是南亚和东亚季风的交汇处,也是大气变化的敏感区和热源区。开展该地区地气相互作用对区域水热过程影响机制及其参数化研究,对于研究青藏高原大气水汽传输的关键过程问题有重大意义。本文介绍了基于涡动观测法开展的青藏高原东南缘地区的地气相互作用观测试验,并总结了洱海湖面、丽江高山草甸及腾冲北海湿地的地气交换特征,以及利用数值模式开展复杂山地局地环流特征的研究工作。目前已初步明确和揭示青藏高原东南缘横断山脉不同类型下垫面的地气交换特征及其影响因素,主要结论如下:青藏高原东南缘高山草甸的碳、水交换过程受降水分布影响显著,“浮毯型”湿地(水面常年覆盖有“浮毯”状苔草草排)的碳、水交换除了受气象因素影响外,也受到下垫面植被和水体比例变化的影响。不同类型生态系统的碳、水交换过程在不同时间尺度的影响因子存在差别。风速始终是湖泊潜热和CO_(2)交换的关键影响因子,而降水在较长时间尺度对湖泊CO_(2)通量也有显著影响。此外,青藏高原东南缘的复杂地形对于生态系统的碳、水交换过程也有显著影响。复杂地形产生的不同类型的局地环流对于生态系统的碳、水交换过程有不同的影响。

分布式散射体相位估计奇异值分解法

常规的分布式散射体(DS)相位估计方法需要生成全组合干涉对以构建样本协方差矩阵(SCM),然后根据SCM的统计特性估计DS相位,这一过程不但计算耗时,而且占据大量存储空间。本文提出了一种基于奇异值分解技术的DS相位快速估计方法(SVDI)。该方法分析的对象是单主影像干涉对组成的干涉相位矩阵而非全组合干涉对组成的SCM,因而可以有效提高计算效率、节省存储空间。并且,理论上证明了在一定条件下SVDI的结果与常规的特征值分解方法(EVD)是一致的。模拟数据和真实SAR数据的结果表明,SVDI算法有更高的计算效率,并且其相位估计精度以及形变解算精度与常规算法是一致的。

2013-2020年海河流域农田水热碳通量及气象要素观测数据集

本研究以海河流域官厅水库旁农田生态系统为研究对象,采用蒸渗仪、涡动相关仪、大孔径闪烁仪和自动气象站/气象要素梯度等观测系统,开展农田生态系统多尺度通量和气象要素的长期定位观测。本数据集由多尺度通量(米,百米,公里级)和气象要素数据组成,观测项目包括生态系统净碳交换量、潜热通量/蒸散发、感热通量、空气温度、空气相对湿度、风速、风向、向下/上短波辐射、向下/上长波辐射、净辐射、大气压、降水、红外辐射温度、光合有效辐射、土壤温度、土壤水分、土壤热通量、平均土壤温度等。本数据集经过了严格的处理和质量控制,可用于研究水库消涨对周边生态系统碳水等物质和能量的循环带来的影响,也可为相关遥感模型或过程模型等研究提供有力的数据基础。

高速铁路三维控制网粗差实时判别方法研究

高速铁路三维控制网外业观测易受不利环境因素影响,难免存在测量粗差,为了尽早识别测量粗差,提高外业数据质量及后续平差结果的精度,需进行三维控制网粗差实时判别方法的研究。根据三维控制网网形及其特点,详细分析三维控制网闭合环的类型,采用三维控制网(Ⅰ类、Ⅱ类、Ⅲ类)22种闭合环进行闭合差计算;同时,基于协方差传播定律构建闭合环校核边中误差的数学模型,以2倍闭合环相对中误差为限差,判别三维控制网外业数据质量。研究表明,三维控制网网形具有较强的规律性和对称性,在完成归整后,可使用1/7000,1/13000和1/23000为对应闭合环的极限误差进行粗差判别。研究结果表明,在三维控制网外业数据观测过程中,采用该方法能够实时判别并发现测量粗差,有利于以最小代价进行现场补测,从而提高三维控制网数据可靠性和数据质量。

基于评价核数据库的多群协方差处理程序CovarXS的开发与验证

核数据的不确定度已成为反应堆物理计算结果不确定度的主要来源,而不确定度计算需要的核截面协方差数据处理软件强烈依赖于国外相关计算软件,目前国内对核数据协方差信息处理软件鲜有报道,且对其处理方法和理论研究不多,为此,本文深入研究多群协方差信息处理理论,在自主开发的先进核截面处理程序AXSP中开发了多群协方差处理模块CovarXS,基于ENDF/B-Ⅶ.1评价核数据库制作了33群协方差数据库,通过与NJOY2016中的ERRORR模块计算结果对比,产生的相对协方差数据的相对误差小于1.0%,验证了CovarXS模块开发的正确性。在此基础上,利用制作的33群协方差矩阵对MOX燃料钠冷快堆BN-600基准题进行了分析,对影响系统keff的不确定性最大的核素及其截面进行了分析。基于CovarXS模块和ERRORR计算得到的不同核素对keff的不确定度贡献的相对误差小于0.893%,由此说明CovarXS模块处理得到的多群协方差数据库精度与ERRORR模块生成的多群协方差数据库的精度相当。

基于二次分数低阶协方差的时延估计方法

针对强脉冲噪声背景下基于分数低阶统计量时延估计方法性能退化且需要噪声先验知识的问题,提出了一种基于二次分数低阶协方差的时延估计新方法。所提方法首先利用有界非线性Sigmoid函数对含有脉冲噪声的信号进行预处理,使其在不影响有用信号时延信息的基础上对附加脉冲噪声进行充分压缩;然后对处理后的收发信号进行二次分数低阶协方差运算,即求得发射信号的自分数低阶协方差和收发信号的互分数低阶协方差之后,再次计算二者的互分数低阶协方差,以期更大程度上抑制脉冲噪声的影响。通过模拟仿真实验对所提方法进行了有效性验证,结果表明所提方法突破了分数低阶矩阶次需小于Alpha稳定分布噪声特征指数的限制,并且比分数低阶协方差方法具有更高的估计精度。仿真实验结果表明在广义信噪比-10 dB情况下,时延估计用时为0.0560 s,准确率达到97.76%。

基于扩展卡尔曼滤波的交互式多模型跟踪算法研究

在辅助驾驶系统中,行人轨迹跟踪一直是一项有挑战性的任务,因为行人的回波信号中往往存在着许多干扰噪声。此外,行人在运动过程中可能会做出突然转身或其他改变方向的行为,这将直接导致行人运动轨迹呈现出非线性特征。针对上述问题,文中提出一种基于扩展卡尔曼滤波的交互式多模型跟踪(IMM-EKF)方法,适用于毫米波雷达对行人进行轨迹跟踪。首先,在扩展卡尔曼滤波算法(EKF)的基础上重构状态预测协方差矩阵,来补偿EKF非线性化过程中引入的误差;然后将改进的EKF作为交互式多模型算法(IMM)中的滤波器,根据行人运动特性选择匀速模型和协调转弯模型作为跟踪模型,利用所提出的IMM-EKF算法进行轨迹跟踪。实验结果表明,所提出的滤波算法较典型的EKF和改进的EKF算法,在跟踪滤波精度方面均有所提升,同时具备更优的跟踪鲁棒性。

一种基于实测飞行轨迹的惯导误差分析方法研究

针对传统蒙特卡罗仿真方法用于惯导误差分析时计算量大且参数调整不便的问题,提出了采用Kalman滤波的状态均方误差预测公式结合惯导误差传播方程进行误差协方差分析的新方法。该方法将惯导总误差分解为各种不同误差因素的线性组合,且各误差因素之间是相互独立的,误差计算和参数调整都非常简单方便。以一组实测飞行轨迹数据为例,利用所提协方差分析方法进行误差分解,并将结果以多种形式直观展示,显示了新方法在误差分配和精度评估中的优越性。

纵向多分类数据的广义估计方程分析

广义估计方程(GEE)是分析纵向数据的常用方法.如果响应变量的维数是一,XIE和YANG(2003)及WANG(2011)分别研究了协变量维数是固定的和协变量维数趋于无穷时,GEE估计的渐近性质.本文研究纵向多分类数据(multicategorical data)的GEE建模和GEE估计的渐近性质.当数据的分类数大于二时,响应变量的维数大于一,所以推广了文献的相关结果.

基于小快拍场景的联合校正稳健波束形成算法

针对传统的自适应波束形成算法在目标导向矢量失配及接收数据的协方差矩阵存在误差时,性能急剧下降的问题,提出了一种基于小快拍场景的联合协方差矩阵重构,及导向矢量优化的稳健波束形成算法。对不确定集约束求解得到干扰导向矢量,根据稀疏干扰来向的导向矢量近似正交,求出干扰导向矢量对应的干扰功率,从而完成协方差矩阵重构;对期望信号来向及其邻域进行权值求解,对加权后的数据特征分解,利用多信号分类(Multiple Signal Classification, MUSIC)谱估计算法对信号区域积分得到信号协方差矩阵,将其主特征值近似为期望信号的导向矢量完成重新估计。仿真结果表明,在无误差时,算法输出信干噪比(Signal to Interference Plus Noise Ratio, SINR)接近理论最优;在多种误差环境下输出性能随信噪比(Signal to Noise Ratio, SNR)的变化均具有较好的稳健性,并且在信号来向可精准形成波束;在小快拍时可以较快收敛至理论最优值。

城市轨道交通轨面短波不平顺谱实测研究

针对城市轨道交通诱发的振动噪声问题,对轨面短波不平顺进行研究.首先,对上海市轨道交通线路轨面短波不平顺实测数据进行零均值化、剔除异常值、消除趋势项等预处理.其次,利用改进的协方差法计算得到轨面短波不平顺功率谱,分析轨道类型、曲线半径及钢轨接头对轨面短波不平顺功率谱分布规律的影响.然后,基于非线性最小二乘法对轨面短波不平顺谱进行拟合.最后,提出我国城市轨道交通轨面短波不平顺统计谱表达式.研究结果表明:直线段接头区轨面短波不平顺功率谱密度曲线在整个分析波长范围内具有较多特征峰值;直线段接头区上、下行短波不平顺功率谱密度值随波长的分布特征和数值相差不大,说明上、下行的轨面不平顺状态基本相同;钢轨的磨耗程度与曲线半径呈负相关,内、外轨面不平顺功率谱密度的差异随曲线半径的增大而减小,曲线半径是影响轨面不平顺状态的主要因素之一;线路类型对轨面短波不平顺功率谱的影响不大,不同曲线半径、焊接接头影响明显,在对轨道进行维护和养护时需关注小曲线半径区段以及焊接接头区段的不平顺状态.