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实现The Kalman Filter 机器人的自定位-The Kalman Filter is a technique from estimation theory that combines
the information of dierent uncertain sources to obtain the values of vari
ables of interest together with the uncertainty in these. The lter has b
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这里有卡尔曼滤波原理及公式陈列,编程实现,外加外界白噪声的影响,旨在加强对卡尔曼滤波的直觉理解与应用。-There Kalman filter theory and formula display, programming, plus white noise of the outside world to enhance intuitive understanding of the Kalman filter and its application.
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In his 1960 famous publication (“A new approach to linear filtering and prediction problems”, Trans.ASME J. Basic Engineering., vol 82, March 1960, pp 34-45), Rudolf Kalman based the construction of the state estimation filter on probability theory,
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本文对于非线性非高斯问题,提出了一种改进扩展卡尔曼滤波(NIEKF)新方法。该方法将迭代滤波理论引入到扩展卡尔曼滤波器方法中,有效地重复利用新的测量信息,还利用Levenberg-Marquardt 方法调整预测协方差阵以保证算法具有全局收敛性。实验结果表明,所提方法具有更高的估计精度,是一种效率较高、性能较好的跟踪方法。-This non-Gaussian for nonlinear problems, an improved extended Kalman filter (NIEKF) th
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研究了一类离散不确定系统中存在等式约束时的最优滤波问题,在均方误差最小的意义下利用卡尔曼滤波给出了最优解。与传统的不确定滤波结果相比,从理论证明了利用更多信息的约束滤波的估计误差协方差的迹更小。-A class of discrete uncertain systems exist in the optimal filter when the equality constraint problem, the minimum mean square error in the sense of Ka
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卡尔曼滤波器的算法C实现
最佳线性滤波理论起源于40年代美国科学家Wiener和前苏联科学家Kолмогоров等人的研究工作,后人统称为维纳滤波理论。从理论上说,维纳滤波的最大缺点是必须用到无限过去的数据,不适用于实时处理。为了克服这一缺点,60年代Kalman把状态空间模型引入滤波理论,并导出了一套递推估计算法,后人称之为卡尔曼滤波理论。卡尔曼滤波是以最小均方误差为估计的最佳准则,来寻求一套递推估计的算法,其基本思想是:采用信号与噪声的状态空间模型,利用前一时刻地估计值和现时刻的观测值来
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The function alphaBetaFilter implements a generic algorithm for an alpha-beta filter that is a linear state estimation for position and velocity given an observed data. It acts like a smoothing. Also closely related to Kalman filters and to linear st
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The Kalman filter, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that t
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各种kalman滤波器的设计,pwm整流器的建模仿真,包括最小二乘法、SVM、神经网络、1_k近邻法,D-S证据理论数据融合,虚拟力的无线传感网络覆盖,包括广义互相关函数GCC时延估计。-Various kalman filter design, Modeling and simulation pwm rectifier Including the least squares method, the SVM, neural networks, 1 _k neighbor method, D-S
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现代信号处理中谱估计在matlab中的使用,各种kalman滤波器的设计,包含了阵列信号处理的常见算法,D-S证据理论数据融合,用MATLAB实现的压缩传感。-Modern signal processing used in the spectral estimation in matlab, Various kalman filter design, Contains a common array signal processing algorithm, D-S evidence theory
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卡尔曼滤波(Kalman filtering)一种利用线性系统状态方程,通过系统输入输出观测数据,对系统状态进行最优估计的算法。由于观测数据中包括系统中的噪声和干扰的影响,所以最优估计也可看作是滤波过程。
斯坦利·施密特(Stanley Schmidt)首次实现了卡尔曼滤波器。卡尔曼在NASA埃姆斯研究中心访问时,发现他的方法对于解决阿波罗计划的轨道预测很有用,后来阿波罗飞船的导航电脑使用了这种滤波器。 关于这种滤波器的论文由Swerling (1958), Kalman (1960)与 Ka
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