共查询到19条相似文献,搜索用时 843 毫秒
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动态卡尔曼滤波在导航试验状态估计中的应用 总被引:20,自引:9,他引:11
阐述了GPS动态试验的新方案,使用两个精度相差一个数量级的GPS接收平台,通过匀速运动车辆的DGPS及GPS的滤波对比试验,验证了卡尔曼滤波器的有效性.并针对传统EKF(extended Kalman filtering)滤波器动态滤波性能较差的缺陷,引入了一种基于非线性思想的动态无导数卡尔曼滤波器,并对其状态方差阵及随机噪声方差阵Cholesky分解更新公式做了改进,避免了导数的运算,加快了滤波速度,有效地确保方差矩阵平方根的正定性从而抑止了发散.将这种新的卡尔曼滤波器应用于实际动态定位状态估计问题上.试验结果表明:比起传统卡尔曼滤波器,新的卡尔曼滤波器有较高的精度,实用性更强. 相似文献
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《机械工程与自动化》2020,(1)
索道轿厢的实时精准定位在客运索道安全运营中至关重要,而GPS信号在索道运营区域常常出现信号不稳定、定位精度不高等问题。提出了一种基于卡尔曼滤波器的索道轿厢相对定位方法,研究了GPS经纬度与索道轿厢位移的换算关系,建立了适合于索道轿厢GPS定位的卡尔曼滤波器模型。实验结果表明:所提出的方法不仅满足了定位精准度,又保证了数据的实时性,其结果符合索道的运营要求。本方法已成功应用于索道行业,为国内诸多知名景区的索道轿厢定位提供了解决方案。 相似文献
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六维力传感器能够通过应变片及板梁结构实时检测空间六方向的力信息,但其输出信号不可避免地被噪声干扰所污染。为改善这一现象,同时针对过程噪声模型不精准致使经典卡尔曼滤波器性能差的问题,设计了一种双因子渐消卡尔曼滤波器。算法研究了加性噪声信号的统计特性,建立了矩形板主振型增广状态方程,分析了两种过程噪声模型偏差对滤波性能的影响。在经典卡尔曼滤波器的基础上,基于新息正交性原理,依据Sage开窗估计原理与最小二乘准则,构造了双渐消因子的解析式,阐述了滤波器的工作原理。研究表明:双渐消卡尔曼滤波器稳定性强,能够有效削弱噪声模型偏差的影响;对比抗差卡尔曼滤波器,精度提升38.66%。 相似文献
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卡尔曼滤波器是线性动态系统中应用最广泛的一种状态估计方法。在非线性系统中,扩展卡尔曼滤波(EKF)和无迹卡尔曼滤波(UKF)被广泛应用,相比扩展卡尔曼滤波器,无迹卡尔曼滤波器准确度更高、更易于实现。在车辆动力学这种强的非线性系统中,无迹卡尔曼滤波器应用广泛。设计了一种基于无迹卡尔曼滤波器的半主动悬架系统状态观测器,讨论了不准确的过程噪声协方差Q和测量噪声协方差R、及测量信号组合的选择和不准确的模型参数对状态观测精度的影响,仿真结果表明不准确的过程噪声和测量噪声协方差、不合适的测量信号选择和模型参数不准确的干扰在不同程度上降低了状态估计精度。 相似文献
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高频信号注入法估计永磁同步电机转子位置的精度,依赖于高频电流信号的提取.卡尔曼滤波器是对付白噪声的有力工具,本文针对高频注入信号的特点,设计了卡尔曼滤波器,并进行了仿真和实验研究.结果表明,经过卡尔曼滤波器的处理,电流信号信噪比大大提高. 相似文献
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在移动机器人姿态估计过程中,由于难以建立准确的系统模型,导致参数量测不准确。采用一种自适应卡尔曼滤波器,通过加入遗忘因子的方式改变滤波器对状态估计的信任程度,改善了系统中噪声统计模型不准确对测量结果的影响。仿真结果表明,当经验知识不足导致建立姿态测量模型不准确时,自适应卡尔曼滤波器的滤波效果优于传统卡尔曼滤波器,提高了姿态信号的估计精度。 相似文献
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《Measurement》2016
GPS/INS integrated system is very subject to uncertainties due to exogenous disturbances, device damage, and inaccurate sensor noise statistics. Conventional Kalman filer has no robustness to address system uncertainties which may corrupt filter performance and even cause filter divergence. Based on the INS error dynamic equation, a robust Kalman filter is analyzed and applied in loosely coupled GPS/INS integration system. The norm bounded robust Kalman filter, with recursive form by solving two Riccati equations, guarantees a estimation variance bound for all the admissible uncertainties, and can evolve into the conventional Kalman filter if no uncertainties are considered. This paper will analyze the suitable case for the robust Kalman filter in GPS/INS system, the filter characteristics including parameter setting, parameter meaning, and filter convergence condition are discussed simutaneously. The robust filter performance will be compared with conventional Kalman filter through simulation results. 相似文献
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Aiming to improve positioning precision of the GPS/INS integrated navigation system during GPS outages, a novel model combined with strong tracking Kalman filter (STKF) and wavelet neural network (WNN) algorithms for INS errors compensation is proposed and tested. STKF is used to estimate INS errors as a replacement of Kalman filter (KF), and WNN is applied to establish a highly accurate model based on STKF when GPS works well and to predict INS errors during GPS outages. Performance of the proposed model has been experimentally verified using GPS and INS data collected in a land vehicle navigation test. The comparison results indicate that the proposed model combined with STKF/WNN algorithms can effectively provide high accurate corrections to the standalone INS during GPS outages. 相似文献
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基于手机惯性传感器的行人航位推算方法是行人导航的核心方法之一。 然而由于传感器噪声等因素,航位推算获取
的位置信息误差往往随着时间发散,通常将航位推算和卫星导航通过卡尔曼滤波构成组合导航系统,利用卫星提供的高精度定
位信息补偿航位推算误差。 提出一种基于图优化的行人协同定位方法,将状态转移、量测和协同测距信息都作为状态的约束,
统一进行优化估计。 为验证方法的有效性,分别在卫星信号良好、无卫星环境下进行了实验验证。 实验分析结果表明,基于图
优化的行人协同定位方法在有无卫星信号情况下,都可以有效地提升系统的定位精度。 和基于卡尔曼滤波的协同方法相比,最
大水平定位误差都减少了 30% 以上。 相似文献
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针对传统卡尔曼滤波算法在进行车辆实时运动过程中难以精准定位问题,提出一种基于运动状态自适应的交互多模型卡尔曼滤波(Interacting multiple model Kalman filter,IMMKF)与多基站到达方向(Direction-of-arrival,DOA)相融合进行车辆位置实时估计算法。基于无偏估计器对测量噪声协方差进行实时更新并将其嵌入标准卡尔曼滤波算法中实现自适应交互多模型卡尔曼滤波。针对车辆不同运动状态及动态行驶环境对车辆定位估计精度的影响,构建自适应交互多模型卡尔曼滤波器与多基站信息融合算法进行车辆位置实时估计,考虑不同车速与不同基站数等行驶工况下车辆定位精度的变化趋势,实现车辆实时位置的准确估计。利用PreScan-Simulink联合仿真平台进行虚拟仿真验证和实车试验验证。结果表明,基于交互多模型卡尔曼滤波与到达方向角的融合算法相对标准的卡尔曼滤波估计精度高,较好地改善了传统单一模型的卡尔曼滤波算法在进行车辆实时运动状态估计过程中精准定位问题,实车试验验证了提出算法对车辆定位精度较传统卡尔曼滤波算法的精度提高了一个数量级,实现了更精确的车辆位置估计。 相似文献
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《Measurement》2016
In kinematic position estimation, a Kalman filter procedure is often used to provide improved solution benefiting from the history information. However, the optimal Kalman filtering solutions are subject to precise function models and statistic knowledge of noises, which may be difficult to obtain in advance. As a result, Kalman filter does not necessarily provide better performance for kinematic positioning solutions. In real world situations, a bound of the noise distribution would be easily and more reasonably determined than noise statistics. This paper studies ellipsoid bounding estimation for kinematic position estimation. In this estimation, neither process nor measurement noise characteristics are necessary, as long as the noises at each sample points can be confined in a bound (ellipsoid). A general trace criteria is adopted to choose the optimal estimator. For a the special case that only scalar measurement is available, e.g., a position measurement, we designed a modified intersection approach to reduce the estimation conservatism. Numerical results are given in each estimation step to illustrate the algorithm. A flight trajectory data is processed and the estimation results are compared under three different measurement noise cases: Gaussian white noise, uniformly noise (non-Gaussian) and the real measurement noise. Kalman filter results are also given for comparison. Results demonstrate the ellipsoid estimation indeed offers improved kinematic position solution in the sense of robustness for non-Gaussian noises, and retains nearly the same estimation error variance. 相似文献
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《Mechanical Systems and Signal Processing》2014,42(1-2):181-193
An experimental evaluation of Bayesian positional filtering algorithms applied to mobile robots for Non-Destructive Evaluation is presented using multiple positional sensing data – a real time, on-robot implementation of an Extended Kalman and Particle filter was used to control a robot performing representative raster scanning of a sample. Both absolute and relative positioning were employed – the absolute being an indoor acoustic GPS system that required careful calibration. The performance of the tracking algorithms are compared in terms of computational cost and the accuracy of trajectory estimates. It is demonstrated that for real time NDE scanning, the Extended Kalman Filter is a more sensible choice given the high computational overhead for the Particle filter. 相似文献
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为解决扩展卡尔曼滤波器(extended Kalman filter,EKF)在车辆组合定位系统中因车辆加减速、转弯(以下简称机动)而存在的精度低、稳定性差等问题,设计了一种将交互多模型(interacting multiple model,IMM)算法与非线性卡尔曼滤波器相融合的自适应滤波算法。该算法使用三种状态空间模型来描述车辆的运动模式,采用多个非线性滤波器对每个模型并行滤波,通过模型匹配似然函数对滤波结果进行加权融合,最终得到系统的定位信息。该方法具备非线性系统滤波器优点,克服了单一模型滤波算法对机动目标定位效果差的缺点。利用该方法和EKF算法分别对GPS/INS/DR车辆组合定位系统中进行了仿真实验,结果表明,该算法的滤波定位精度明显优于目前组合定位系统中所用的EKF滤波器,大幅提高了组合定位系统的稳定性和定位精度。 相似文献
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This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the weak point on position estimation by the merits of GPS and INS.In general,extended Kalman filter(EKF)has been widely used in order to combine GPS with INS.However,UPF can get the position more accurately and correctly than EKF when it is applied to real-system included non-linear,irregular distribution errors.In this paper,the accuracy of UPF is proved through the simulation experiment,using the virtual-data needed for the test. 相似文献