Range-Only UWB SLAM for Indoor Robot Localization Employing Multi-Interval EFIR Rauch-Tung-Striebel Smoother

For improving the localization accuracy,a multi-interval extended finite impulse response(EFIR)-based Rauch-Tung-Striebel(R-T-S)smoother is proposed for the range-only ultra wide band(UWB)simultaneous localization and mapping(SLAM)for robot localization.In this mode,the EFIR R-T-S(ERTS)smoother employs EFIR filter as the forward filter and the R-T-S smoothing method to smooth the EFIR filter’s output.When the east or the north position is considered as stance,the ERTS is used to smooth the position directly.Moreover,the estimation of the UWB Reference Nodes’(RNs’)position is smoothed by the R-T-S smooth method in parallel.The test illustrates that the proposedmulti-interval ERTS smoothing for range-only UWB SLAMis able to provide accurate estimation.Compared with the EFIR filter,the proposed method improves the localization accuracy by about 25.35%and 40.66%in east and north directions,respectively.

Square-root divided difference Rauch-Tung-Striebel smoother

A square-root version of the divided difference Rauch-Tung-Striebel (RTS) smoother is proposed in this paper. The square-root variant essentially propagates the square roots of the covariance matrices and can consistently improve the numerical stability because all the resulting covariance matrices are guaranteed to stay positive semi-definite. Furthermore, the square-root form ensures reliable implementation in an embedded system with fixed or limited precision although it is algebraically equivalent to the standard form. The new smoothing algorithm is tested in a challenging two-dimensional maneuvering target tracking problem with unknown and time-varying turn rate, and its performance is compared with that of other de-facto standard filters and smoothers. The simulation results indicate that the proposed RTS smoother markedly outperforms the associated filters and gives slightly smaller error than an unscented-based RTS smoother.

Bridging element-free Galerkin and pluri-Gaussian simulation for geological uncertainty estimation in an ensemble smoother data assimilation framework

The facies distribution of a reservoir is one of the biggest concerns for geologists,geophysicists,reservoir modelers,and reservoir engineers due to its high importance in the setting of any reliable decisionmaking/optimization of field development planning.The approach for parameterizing the facies distribution as a random variable comes naturally through using the probability fields.Since the prior probability fields of facies come either from a seismic inversion or from other sources of geologic information,they are not conditioned to the data observed from the cores extracted from the wells.This paper presents a regularized element-free Galerkin(R-EFG)method for conditioning facies probability fields to facies observation.The conditioned probability fields respect all the conditions of the probability theory(i.e.all the values are between 0 and 1,and the sum of all fields is a uniform field of 1).This property achieves by an optimization procedure under equality and inequality constraints with the gradient projection method.The conditioned probability fields are further used as the input in the adaptive pluri-Gaussian simulation(APS)methodology and coupled with the ensemble smoother with multiple data assimilation(ES-MDA)for estimation and uncertainty quantification of the facies distribution.The history-matching of the facies models shows a good estimation and uncertainty quantification of facies distribution,a good data match and prediction capabilities.

容积Rauch-Tung-Striebel平滑器

针对离散非线性系统的状态平滑问题,基于Rauch-Tung-Striebel(RTS)理论设计了一种容积卡尔曼平滑器(Cubature Kalman Smoother,CKS),即容积Rauch-Tung-Striebel平滑器(RTSCKS)。首先,基于经典贝叶斯状态估计理论框架,推导了状态概率密度分布形式的非线性系统最优平滑算法;其次,基于Rauch-Tung-Striebel理论,建立了相应的最优平滑递推算法;然后,将其与容积卡尔曼滤波算法相结合,建立了递推形式的RTS-CKS平滑器;最后,通过典型的纯方位跟踪模型验证了该平滑器的可行性和有效性。该平滑器为非线性系统的状态估计提供了新的估计算法。

一种新的固定点容积Rauch-Tung-Striebel平滑算法

针对在目标跟踪实际应用中对于观测时间区间的某个固定时刻估计值精度的要求,提出了一种运用于非线性模型中的目标跟踪算法——固定点容积Rauch-Tung-Striebel平滑。该算法将高斯最优平滑中固定点平滑策略与传统的运用于非线性状态空间模型的容积Rauch-Tung-Striebel平滑相结合,有效地提高了固定点估计值的精度。仿真结果验证了新算法的有效性。

基于Super smoother和3σ原理的列车动态测试趋势性异常数据清洗方法与分析

列车动态测试存在数据采集环境干扰大,重复成本高等问题,需要尽可能的从存在异常的数据中保留更多的有效信息。本文针对其中存在的长周期,低频率的趋势性异常数据清洗问题,首先介绍了一种基于Super smoother和3σ原理的数据清洗方法。然后通过与其他常用异常数据清洗方法如神经网络,小波变换等的对比,分别从降噪处理,数据漂移处理,缺失数据补充处理和短暂快速异常波动处理四个方面对方法的数据清洗能力进行了分析和验证,结果表明清洗后数据的Pearson系数由0.785上升到0.923,方法在快速清洗和数据修补方面具有较大优势。最后以某城轨列车制动温升试验数据为例,对实际线路测试数据进行了数据清洗处理,结果表明方法能够较好的解决列车动态测试中存在的趋势性异常数据清洗问题。

Multi-sensor optimal weighted fusion incremental Kalman smoother

In practical applications, the system observation error is widespread. If the observation equation of the system has not been verified or corrected under certain environmental conditions,the unknown system errors and filtering errors will come into being.The incremental observation equation is derived, which can eliminate the unknown observation errors effectively. Furthermore, an incremental Kalman smoother is presented. Moreover, a weighted measurement fusion incremental Kalman smoother applying the globally optimal weighted measurement fusion algorithm is given.The simulation results show their effectiveness and feasibility.

Groundwater contaminant source identification based on iterative local update ensemble smoother

Identification of the location and intensity of groundwater pollution source contributes to the effect of pollution remediation,and is called groundwater contaminant source identification.This is a kind of typical groundwater inverse problem,and the solution is usually ill-posed.Especially considering the spatial variability of hydraulic conductivity field,the identification process is more challenging.In this paper,the solution framework of groundwater contaminant source identification is composed with groundwater pollutant transport model(MT3DMS)and a data assimilation method(Iterative local update ensemble smoother,ILUES).In addition,Karhunen-Loève expansion technique is adopted as a PCA method to realize dimension reduction.In practical problems,the geostatistical method is usually used to characterize the hydraulic conductivity field,and only the contaminant source information is inversely calculated in the identification process.In this study,the identification of contaminant source information under Kriging K-field is compared with simultaneous identification of source information and K-field.The results indicate that it is necessary to carry out simultaneous identification under heterogeneous site,and ILUES has good performance in solving high-dimensional parameter inversion problems.

Smoother manifold for graph meta-learning

Meta-learning provides a framework for the possibility of mimicking artificial intelligence.How-ever,data distribution of the training set fails to be consistent with the one of the testing set as the limited domain differences among them.These factors often result in poor generalization in existing meta-learning models.In this work,a novel smoother manifold for graph meta-learning(SGML)is proposed,which derives the similarity parameters of node features from the relationship between nodes and edges in the graph structure,and then utilizes the similarity parameters to yield smoother manifold through embedded propagation module.Smoother manifold can naturally filter out noise from the most important components when generalizing the local mapping relationship to the global.Besides suiting for generalizing on unseen low data issues,the framework is capable to easily perform transductive inference.Experimental results on MiniImageNet and TieredImageNet consistently show that applying SGML to supervised and semi-supervised classification can improve the performance in reducing the noise of domain shift representation.

Optimal and suboptimal white noise smoothers for nonlinear stochastic systems

A new approach of smoothing the white noise for nonlinear stochastic system was proposed. Through presenting the Gaussian approximation about the white noise posterior smoothing probability density fimction, an optimal and unifying white noise smoothing framework was firstly derived on the basis of the existing state smoother. The proposed framework was only formal in the sense that it rarely could be directly used in practice since the model nonlinearity resulted in the intractability and infeasibility of analytically computing the smoothing gain. For this reason, a suboptimal and practical white noise smoother, which is called the unscented white noise smoother （UWNS）, was further developed by applying unscented transformation to numerically approximate the smoothing gain. Simulation results show the superior performance of the proposed UWNS approach as compared to the existing extended white noise smoother （EWNS） based on the first-order linearization.

Design of RLS Wiener Smoother and Filter for Colored Observation Noise in Linear Discrete-Time Stochastic Systems

Almost estimators are designed for the white observation noise. In the estimation problems, rather than the white observation noise, there might be actual cases where the observation noise is modeled by the colored noise process. This paper examines to design a new estimation technique of recursive least-squares (RLS) Wiener fixed-point smoother and filter for colored observation noise in linear discrete-time wide-sense stationary stochastic systems. The observation y(k) is given as the sum of the signal z(k)=Hx(k) and the colored observation noise vc(k). The RLS Wiener estimators explicitly require the following information: 1) the system matrix for the state vector x(k);2) the observation matrix H;3) the variance of the state vector x(k);4) the system matrix for the colored observation noise vc(k);5) the variance of the colored observation noise;6) the input noise variance in the state equation for the colored observation noise.

基于相关性局域化迭代集合平滑反演渗透系数场

在地下水流和溶质运移问题中,有较多研究基于物理距离局域化集合同化方法反演水文地质参数。当反演参数与观测信息之间不存在物理距离时,这种方法不适用。为了克服这个局限,通过渗透系数与水头信息之间的相关性计算局域化方法的阻滞因子,构建基于相关性的局域化迭代集合平滑方法。为了方便比较,将该方法和一种基于物理距离的局域化迭代集合平滑一同用于同化水头信息反演二维孔隙承压含水层的渗透系数场。算例中考虑了不同集合大小、观测误差及观测数量等因子的组合,便于分析其对渗透系数反演精度的影响。研究结果显示:(1)在所有算例中新方法得到的渗透系数均方根误差范围为[0.8307,0.9590],都小于基于物理距离方法的均方根误差,范围为[0.8394,1.0000];(2)基于物理距离方法得到的渗透系数场空间上存在不连续性,而新方法的结果不存在此现象。文章提出了一种新的基于相关性局域化迭代平滑方法,该方法不需要依赖参数与观测信息之间的物理距离且参数反演精度高于基于物理距离的方法,可作为参数反演的科学工具。

基于厚尾双学生氏t分布的非线性状态空间系统鲁棒辨识方法

状态空间模型作为一种常见且重要的模型结构在自动化领域有着广泛的应用,本文针对异常值干扰下的非线性状态空间系统辨识问题开展研究.与现有的辨识方法不同,本文充分考虑了状态转移过程和输出量测过程均受到异常值干扰的情况,提出了一种更加全面的鲁棒辨识算法.首先利用两个相互独立的学生氏t分布分别对状态噪声和输出噪声进行建模以保障算法的鲁棒性;其次利用粒子平滑算法估计状态变量的后验概率分布以解决状态未知问题;最后利用期望最大化算法实现未知参数估计.在算法实现过程中使用了学生氏t分布表达式的数学分解,这样做的好处是:(1)更加有利于算法的推导和实现;(2)更清晰地解释了算法的鲁棒性能.并且本文通过数值算例和应用算例验证了该方法的有效性.

基于EKS的风电并网系统频率特征参数辨识算法

新型电力系统建设背景下系统对接入风电的频率调节能力提出了要求。但风电在可调容量耗尽时将退出调频,导致系统等效惯量等参数和频率响应特性变化,影响系统切机切负荷等频率控制措施的正确动作。为此,提出一种基于扩展卡尔曼平滑算法(Extented Kalman Smoother, EKS)的风电并网系统频率特征参数辨识方法,可实现不同工况下系统频率特征参数的高效准确辨识。首先分析了风电场频率控制方法,建立了基于共模分量的风电并网系统频率响应模型,然后分析了EKS算法,进而提出了基于EKS的系统频率特征参数辨识方法,最后通过仿真算例进行了验证。仿真结果表明,所提方法可有效辨识不同工况下系统的惯量、阻尼等频率特征参数。

一种新的估计非高斯分布含水层渗透系数场的方法

集合卡尔曼滤波(ensemble Kalman filter,EnKF)是最流行的数据同化方法之一。然而,在处理非高斯问题时,EnKF存在局限性。为了解决非高斯问题并准确描述含水介质连通性,将正态分数变换(normal-score transformation,NST)与多重数据同化集合平滑器(ensemble smoother with multiple data assimilation,ES-MDA)相结合,提出NS-ES-MDA方法。通过对比实验,验证了NS-ES-MDA方法估计非高斯分布含水层渗透系数场的有效性。相较于重启正态分数集合卡尔曼滤波器(restart normal-score ensemble Kalman filter,rNS-EnKF)方法,NS-ES-MDA在吸收相同数据后,参数估计精度提升约34%,计算效率提升约35%。此外,NS-ES-MDA方法受“异参同效”现象的影响较小,具有较强的更新能力,能够保障得到较准确的参数估计值。研究可为非高斯分布含水层参数估计提供一种有效的求解方法。

基于窗口粒子滤波算法的土壤水分同化及滑坡灾害预警

【目的】在水-力耦合计算中,土壤水力参数通过量化土壤含水量与孔隙水压力的转换关系,决定有效应力及边坡稳定性的计算结果。研究稳健、可靠的数据同化方法,降低土壤水力参数的不确定性,提升土壤水动力模拟的准确性,对降雨型滑坡灾害预警具有重要意义。【方法】通过虚拟算例和实例应用,提出将窗口粒子滤波数据同化方法(简称PBS算法)与渗流-边坡稳定分析模型结合,通过同化土壤含水量数据,达到反演土壤水力参数、模拟土壤孔隙水压力以及预测边坡稳定性的目标。通过虚拟算例,证实了当PBS算法设定大于2 d的时间窗口,以及大于80个的粒子(参数样本)时,能够获得较为准确的模拟结果。实例应用选取四川省都江堰市银洞子沟滑坡堆积体,将PBS算法同化三个位置的土壤含水量的野外监测数据,以4 d为窗口,更新100个粒子样本的土壤水力参数。【结果】结果表明,土壤含水量的模拟值与实测值基本吻合,且模拟的孔隙水压力及边坡稳定系数能对降雨做出清晰、有效的响应。在经过2~3个窗口更新后,三个探头孔隙水压力模拟值不确定区间大小均小于0.11 m,边坡稳定系数的不确定区间大小分别为0.03、0.01和0.11。针对2017年8月28日的极端降雨诱发的滑坡灾害事件的预警,经PBS算法同化后的土壤含水量、孔隙水压力以及边坡稳定系数都收敛到较窄的集合区间,且当日低于1.0的边坡稳定系数,可警示滑坡风险。【结论】通过虚拟算例及实际应用,证实了PBS算法可支持稳健、可靠的土壤水力参数估计及渗流过程模拟,在边坡稳定分析及降雨型滑坡灾害预警领域具有广阔的应用价值。

固定区间平滑算法及其在组合导航系统中的应用

对于离线处理或实时性要求不高的组合导航系统,平滑作为一种离线估计算法,通过利用更多的量测信息,能够获得优于滤波估计的结果。固定区间平滑是利用某一时间区间内的所有量测信息对所有状态进行估计的一种算法,在组合导航系统中应用广泛。该算法主要包括适用于线性系统的Rauch-Tung-Striebel平滑算法和双滤波器平滑算法,以及基于上述两类平滑方式的非线性平滑估计算法。针对组合导航系统,详细阐述了这几类固定区间平滑算法并分析了各自的优缺点。

多模型概率假设密度平滑器

针对杂波环境下的多个机动目标跟踪问题,本文将多模型概率假设密度(Multiple-model probability hypothesis density,MM-PHD)滤波器和平滑算法相结合,提出了MM-PHD前向–后向平滑器.为了避免引入复杂的随机有限集(Random finiteset,RFS)理论,本文根据PHD的物理空间(Physical space)描述法推导得到了MM-PHD平滑器的后向更新公式.由于MM-PHD前向–后向平滑器的递推公式中包含有多个积分,因此它在非线性非高斯条件下没有解析的表达形式.故本文又给出了它的序贯蒙特卡洛(Sequential Monte Carlo,SMC)实现.100次蒙特卡洛(Monte Carlo,MC)仿真实验表明,与MM-PHD滤波器相比,MM-PHD平滑器能够更加精确地估计多个机动目标的个数和状态,但MM-PHD平滑器存在一定的时间滞后,并且需要耗费更大的计算代价.

遥感反演时间序列叶面积指数的集合卡尔曼平滑算法

基于MODIS LAI产品数据集(MOD15A2)构建经验性的LAI动态模型,以LAI作为连接参数,将LAI动态模型与植被辐射传输模型MCRM2相耦合,提出了将耦合模型与时间序列MODIS反射率观测数据集(MOD09A1)同化进行LAI反演的方案。将集合卡尔曼平滑(EnKS)方法引入到LAI同化反演中,为更好地评价该算法的适用性,还与集合卡尔曼滤波(EnKF)的LAI反演结果、MODIS LAI产品进行了比较分析。研究结果表明,采用EnKS方法的反演结果较为理想,与EnKF方法和MODIS LAI相比,EnKS方法反演的LAI时间廓线更平滑,更具连续性,符合实际的植被生长规律。基于EnKS方法的LAI反演方案,为提取时间连续的LAI廓线提供了一种有效的途径。

基于灰色模型和Kalman平滑器的多母线短期负荷预测

提出一种基于灰色模型和Kalman平滑器的多母线短期负荷预测方法。首先利用频域分解消去母线负荷序列的周周期分量,建立序列的灰色模型;利用系统负荷预测方法得到系统负荷预测值。然后基于灰色模型,将各母线负荷的累加序列作为状态,系统负荷的累加序列作为观测,建立线性离散随机系统模型,利用Kalman平滑器计算各母线负荷累加序列的最优估计值,最后经过累减还原并加上周周期分量得到母线负荷的预测值。Kalman平滑器利用高准确率的系统负荷预测结果对母线负荷预测进行调整,降低预测误差。在实际系统中进行了仿真验证,证明了该方法的有效性。