共查询到20条相似文献,搜索用时 15 毫秒
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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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针对卫星的姿态和角速度估计问题,分别给出基于Unscented卡尔曼滤波(UKF)与推广卡尔曼滤波(EKF)的估计算法,并做了相应比较.为了避免欧拉角带来的奇异问题,UKF选用Rodrigues参数而EKF选用四元数参数法来描述姿态误差.考虑卫星的非线性模型,UKF采用Unscented变换而EKF采用线性化方法对姿态误差进行估计.利用陀螺和磁强计的测量信息,KF和EKF都可得到三轴稳定卫星的姿态估计值,但UKF的收敛速度高于EKF.数值仿真结果表明,当初始姿态存在大偏差时,所给出的UKF的滤波算法性能明显优于EKF. 相似文献
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研究了无线传感器网络中的分布式鲁棒状态信息融合问题. 在局部状态估计层, 基于鲁棒统计学理论提出了适用于噪声相关情况的抗差(扩展)卡尔曼滤波器. 在融合中心层, 针对局部估计相关未知性和不完整性, 给出了不依赖于互协方差阵的稳健航迹融合方法—–内椭球逼近法. 仿真结果证实了算法的有效性: 所提出的抗差卡尔曼滤波器在野值存在情况下, 性能退化远低于传统卡尔曼滤波器(28.6%比428.6%); 所提出的内椭球逼近法获得比协方并交叉法更好的融合估计性能, 且不需要局部估计相关性的先验知识. 相似文献
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将粒子滤波理论和宏观随机交通流模型结合,对高速公路交通状态进行实时估计。在该方法中,高速公路被看作是由等距离的路段首尾相接而形成的模型,交通传感器通常设置在路段的交界处,而且数量远少于所需估计的交通状态。采用压缩状态空间的形式,将模型参数也作为交通状态而非常量进行估计。仿真结果表明粒子滤波方法能够有效地估计和跟踪交通状态的变化,并且与扩展卡尔曼滤波方法相比具有更高的精确度。 相似文献
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基于扩展卡尔曼滤波的船舶横向运动扰动估计 总被引:2,自引:0,他引:2
建立了船舶横向运动状态方程和测量方程,利用扩展卡尔曼滤波方法对海浪扰动下的船舶横向运动的扰动力和力矩作出估计。仿真结果表明,扩展卡尔曼滤波法比有色卡尔曼滤波法估计效果更优。 相似文献
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程水英 《计算机工程与应用》2008,44(24):25-35
综述了非线性估计问题的由来、无味变换(UT,Unscented Transformation)的基本思路与基本算法、各种衍变形式、σ点集的设计原则、无味卡尔曼滤波(UKF,Unscented Kalman Filtering)的基本算法及其各种改进算法、UT的本质、UKF与几种免微分非线性滤波方法的比较、UT与UKF的相关应用、针对几种UKF算法的仿真实例,以及目前在UT与UKF的研究中尚存在的一些问题和对今后研究的展望等;提出了笔者的一些最新研究成果和见解。 相似文献
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In recent years particle filters have been applied to a variety of state estimation problems. A particle filter is a sequential Monte Carlo Bayesian estimator of the posterior density of the state using weighted particles. The efficiency and accuracy of the filter depend mostly on the number of particles used in the estimation and on the propagation function used to re-allocate weights to these particles at each iteration. If the imprecision, i.e. bias and noise, in the available information is high, the number of particles needs to be very large in order to obtain good performances. This may give rise to complexity problems for a real-time implementation. This kind of imprecision can easily be represented by interval data if the maximum error is known. Handling interval data is a new approach successfully applied to different real applications. In this paper, we propose an extension of the particle filter algorithm able to handle interval data and using interval analysis and constraint satisfaction techniques. In standard particle filtering, particles are punctual states associated with weights whose likelihoods are defined by a statistical model of the observation error. In the box particle filter, particles are boxes associated with weights whose likelihood is defined by a bounded model of the observation error. Experiments using actual data for global localization of a vehicle show the usefulness and the efficiency of the proposed approach. 相似文献
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In this paper, a mobile robot control law for corridor navigation and wall-following, based on sonar and odometric sensorial information is proposed. The control law allows for stable navigation avoiding actuator saturation. The posture information of the robot travelling through the corridor is estimated by using odometric and sonar sensing. The control system is theoretically proved to be asymptotically stable. Obstacle avoidance capability is added to the control system as a perturbation signal. A state variables estimation structure is proposed that fuses the sonar and odometric information. Experimental results are presented to show the performance of the proposed control system. 相似文献
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On-line state and parameter estimation of EPDM polymerization reactors using a hierarchical extended Kalman filter 总被引:1,自引:0,他引:1
Rujun Li Armando B Corripio Michael A Henson Michael J Kurtz 《Journal of Process Control》2004,14(8):3403-852
A hierarchical extended Kalman filter (EKF) design is proposed to estimate unmeasured state variables and key kinetic parameters in a first principles model of a continuous ethylene–propylene–diene polymer (EPDM) reactor. The estimator design is based on decomposing the dynamic model into two subsystems by exploiting the triangular model structure and the different sampling frequencies of on-line and laboratory measurements directly related to the state variables of each subsystem. The state variables of the first subsystem are reactant concentrations and zeroth-order moments of the molecular weight distribution (MWD). Unmeasured state variables and four kinetic parameters systematically chosen to reduce bias are estimated from frequent and undelayed on-line measurements of the ethylene, propylene, diene and total polymer concentrations. The state variables of the second subsystem are first-order moments of the MWD. Given state and parameters estimates from the first subsystem EKF, the first-order moments and three non-stationary parameters added to the model for bias reduction are estimated from infrequent and delayed laboratory measurements of the ethylene and diene contents and number average molecular weight of the polymer. Simulation tests show that the hierarchical EKF generates satisfactory estimates even in the presence of measurement noise and plant/model mismatch. 相似文献
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In this paper, we consider the design problem of optimal sensor quantization rules (quantizers) and an optimal linear estimation fusion rule in bandwidth-constrained decentralized random signal estimation fusion systems. First, we derive a fixed-point-type necessary condition for both optimal sensor quantization rules and an optimal linear estimation fusion rule: a fixed point of an integral operation. Then, we can motivate an iterative Gauss–Seidel algorithm to simultaneously search for both optimal sensor quantization rules and an optimal linear estimation fusion rule without Gaussian assumptions on the joint probability density function (pdf) of the estimated parameter and observations. Moreover, we prove that the algorithm converges to a person-by-person optimal solution in the discretized scheme after a finite number of iterations. It is worth noting that the new method can be applied to vector quantization without any modification. Finally, several numerical examples demonstrate the efficiency of our method, and provide some reasonable and meaningful observations how the estimation performance is influenced by the observation noise power and numbers of sensors or quantization levels. 相似文献
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《Journal of Process Control》2014,24(9):1371-1381
This paper deals with the estimation of the plate temperature in heavy plate rolling. An Extended Kalman Filter (EKF) is designed and its initialization and parametrization are discussed in detail. The observer is validated by means of experimental data recorded during a measurement campaign with a special developed test plate instrumented with thermocouples. It is shown that a good estimation accuracy can be achieved under normal production conditions despite the scarcity of measurements. Furthermore, the coupling effect between the plate and the work rolls is investigated in more detail. Based on this analysis, the validity of a computationally inexpensive reduced observer is demonstrated. 相似文献
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Modified Kalman filter for networked monitoring systems employing a send-on-delta method 总被引:1,自引:0,他引:1
In this paper, we consider a networked estimation problem in which sensor data are transmitted only if their values change more than the specified value. When this send-on-delta method is used, no sensor data transmission implies that the sensor value does not change more than the specified value from the previously transmitted sensor value. Using this implicit information, we propose a modified Kalman filter algorithm. The proposed filter reduces sensor data traffic with relatively small estimation performance degradation. Through experiments, we demonstrate the feasibility of the proposed filter algorithm. 相似文献
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Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system 总被引:12,自引:0,他引:12
This paper describes the implementation of an intelligent navigation system, based on the integrated use of the global positioning system (GPS) and several inertial navigation system (INS) sensors, for autonomous underwater vehicle (AUV) applications. A simple Kalman filter (SKF) and an extended Kalman filter (EKF) are proposed to be used subsequently to fuse the data from the INS sensors and to integrate them with the GPS data. The paper highlights the use of fuzzy logic techniques to the adaptation of the initial statistical assumption of both the SKF and EKF caused by possible changes in sensor noise characteristics. This adaptive mechanism is considered to be necessary as the SKF and EKF can only maintain their stability and performance when the algorithms contain the true sensor noise characteristics. In addition, fault detection and signal recovery algorithms during the fusion process to enhance the reliability of the navigation systems are also discussed herein. The proposed algorithms are implemented to real experimental data obtained from a series of AUV trials conducted by running the low-cost Hammerhead AUV, developed by the University of Plymouth and Cranfield University. 相似文献