共查询到20条相似文献,搜索用时 15 毫秒
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基于IEKF的四旋翼无人机姿态测量方法研究 总被引:5,自引:0,他引:5
研究四旋翼无人机在实时飞行时姿态测量的问题.由于四旋翼无人机多采用陀螺仪、加速度计、地磁计(MEMS)传感器测量各姿态,测量值存在偏差,无法直接使用.为解决利用不准确的传感器测量信息获得精确姿态角度的难点,并为了克服常规的扩展卡尔曼算法(EKF)带来的较大滤波偏差,以及四元素法表述复杂等困难,采用了一种改进的迭代EKF(IEKF)算法,利用欧拉角的描述方法,融合MEMS的信息对四旋翼无人机的姿态角度进行测量.实验结果表明,改进方法能准确测量出姿态角,且通过与常规EKF测量效果的比对可以看出,IEKF在测量精度上确有较明显的改善,表明改进方法是有效的,为四旋翼无人机实时调整姿态提供了一种科学手段. 相似文献
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非线性系统的自适应推广的kalman滤波 总被引:13,自引:1,他引:12
本文提出了未知噪声统计的非线性系统中新的自适应推广的Kalman滤波算法.作者提出了用虚拟时变噪声统计[1,2],补偿线性化模型误差的新思想.在本文中,作者指出了文献[3]中,用Sage和Husa的常值噪声统计估值器来估计虚拟噪声是不合理的.另外,即使原非线性系统的噪声统计是零均值,但线性化的模型的噪声统计一般是非零均值的.两个数值模拟例子说明了本文方法的有效性. 相似文献
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卫星轨道估计中广义卡尔曼滤波算法改进 总被引:6,自引:0,他引:6
随着广义卡尔曼滤波越来越广泛的应用,其算法研究越来越深入。文章给出了一种轨道确定方法的广义尔曼滤模型,对广义卡尔曼滤波过程中的状态预报给出了一种修改算法———迭代算法,对这种迭代算法和RKF7(Runge-Kutta-Fehlberg7阶)算法所相应的滤波过程进行了计算机仿真,说明了离散误差对估计轨道的影响,迭代算法可以消减离散误差。通过把两种算法结果进行比较,表明迭代算法简捷、运行较快、且能达到一定的精度。 相似文献
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Huazhen Fang Ning Tian Yebin Wang MengChu Zhou Mulugeta A. Haile 《IEEE/CAA Journal of Automatica Sinica》2018,5(2):401-417
This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date, one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective, which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics (e.g., mean and covariance) conditioned on a system's measurement data. This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering (KF) techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter/input estimation. 相似文献
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自适应扩展卡尔曼滤波(AEKF)通过引入自适应遗忘因子,限制卡尔曼滤波器的记忆长度,充分利用现时的测量数据,增强滤波跟踪性能,具有较好的鲁棒性.本文将AEKF应用到无线传感器网络动态节点的定位中,跟踪移动节点位置.该方法不仅能够实时修正模型误差,还能够自适应调整滤波器的动态范围.仿真分析结果表明,AEKF较之EKF,改善了滤波器的动态性能,较好地抑制了滤波发散过程,具有更好的跟踪性能,提高了定位精度.随着物联网的发展,无线传感器的定位研究将具有非常重要的工程意义和价值. 相似文献
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Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots 总被引:1,自引:0,他引:1
Gerasimos G. Rigatos 《Mathematics and computers in simulation》2010,81(3):590-607
Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots’ state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations. 相似文献
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广义系统Wiener 滤波和Kalman 滤波新方法* 总被引:5,自引:0,他引:5
应用时域上的现代时间序列分析方法,基于ARMA新息模型和白噪声估计理论,提出了广义系统的Wiener状态估值器和急剧记Kalman估值器。它们可统一处理最优滤波,平滑和预后问题。 相似文献
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Søren Hauberg François Lauze Kim Steenstrup Pedersen 《Journal of Mathematical Imaging and Vision》2013,46(1):103-120
In recent years there has been a growing interest in problems, where either the observed data or hidden state variables are confined to a known Riemannian manifold. In sequential data analysis this interest has also been growing, but rather crude algorithms have been applied: either Monte Carlo filters or brute-force discretisations. These approaches scale poorly and clearly show a missing gap: no generic analogues to Kalman filters are currently available in non-Euclidean domains. In this paper, we remedy this issue by first generalising the unscented transform and then the unscented Kalman filter to Riemannian manifolds. As the Kalman filter can be viewed as an optimisation algorithm akin to the Gauss-Newton method, our algorithm also provides a general-purpose optimisation framework on manifolds. We illustrate the suggested method on synthetic data to study robustness and convergence, on a region tracking problem using covariance features, an articulated tracking problem, a mean value optimisation and a pose optimisation problem. 相似文献
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一种改进扩展卡尔曼算法的伪码跟踪优化仿真 总被引:1,自引:0,他引:1
针对当前方法存在伪码跟踪结果不准确的问题,提出基于改进扩展卡尔曼算法的伪码跟踪优化方法。利用振幅、采样时间和码相位等条件计算输入信号,获取到伪码信号的初始位置,建立伪码跟踪环路模型。通过代价函数的计算来分析伪码的跟踪性能,获取状态的预测值和预测方差,并计算代价函数的极小值,利用高精度的迭代方法对方差和协方差展开计算,得到分解因式,计算伪码的容积点,完成对伪码的跟踪,并求得伪码环路模型的解,实现伪码的跟踪优化。实验结果表明,所提方法在对伪码跟踪优化时,具有较好的跟踪性能,并且伪码的相位误差较小,能够准确的完成对伪码的跟踪。 相似文献
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Unscented卡尔曼滤波在状态估计中的应用 总被引:1,自引:1,他引:1
针对非线形系统的滤波问题,无法使用卡尔曼滤波器(KF),扩展卡尔曼滤波(EKF)方法虽能应用于非线形系统,但给出的是状态的有偏估计,并且对模型误差的鲁棒性较差。为了给出更好的状态估计值,该文介绍了Unscented卡尔曼滤波(UKF)的基本原理。其思想是:基于unscented变换,UKF滤波算法能够给出更精确的均值和协方差的估计,从而带来更高的精度。最后通过Mackey—Glass模型时间序列的状态估计仿真实侧说明:同EKF相比,UKF的滤波精度和稳定性都显著提高了,还可避免计算烦琐的Jacobi矩阵,是一种良好的非线性滤波方法。 相似文献
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在基于视频的运动目标检测过程中,经常使用背景差法来检测运动目标。在背景差法中,背景的实时更新是很重要的一个部分,直接影响到检测效果。在研究过去的背景更新方法的基础上,提出一种基于卡尔曼滤波的方法来更新背景,并且把背景模型和当前帧图像的均值和方差等参数与目标检测结果相结合,实现了较好的背景更新结果。算法的复杂度低、实时性好,能够适应工程的需要。 相似文献