Description: 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. Platform: |
Size: 80896 |
Author:闫骁绢 |
Hits:
Description: 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, and more specifically, on the properties of conditional Gaussian
random variables. The criterion he proposed to minimize is the state vector covariance norm, yielding to the classical recursion : the new state estimate is deduced from the previous estimation by addition of a correction term proportional to the prediction error (or the innovation of the measured signal). Platform: |
Size: 104448 |
Author:mohamed |
Hits:
Description: 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) the new method. The method of iterative filtering theory is introduced to the extended Kalman filter method, the effective measurement of repeated use of the new information, also using Levenberg-Marquardt method to adjust the covariance matrix of prediction algorithm to ensure global convergence. Experimental results show that the proposed method has higher estimation accuracy, is a high efficiency, good performance tracking methods. Platform: |
Size: 1527808 |
Author:李辉 |
Hits:
Description: 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 Kalman filtering is given optimal solution. Filtering results with the traditional uncertain than proved from the theory of constraints by using more filter the trace of the covariance estimation error smaller. Platform: |
Size: 199680 |
Author:李辉 |
Hits:
Description: 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 di erent uncertain sources to obtain the values of vari
ables of interest together with the uncertainty in these. The lter has been
successfully applied in many applications, like missions to Mars, and auto
mated missile guidance systems. Although the concept of the lter is rel
atively easy to comprehend, the advantages and shortcomings can only be
understood well with knowledge of the pure basics and with experience. Platform: |
Size: 598016 |
Author:毛玲 |
Hits:
Description: 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更新对状态变量的估计，求出现时刻的估计值。它适合于实时处理和计算机运算。-Kalman filter algorithm implemented in C
Optimal linear filtering theory originated in the 1940s, American scientists Wiener and the former Soviet Union scientists Kолмогоров research, and their descendants are collectively referred to as Wiener filtering theory. In theory, the biggest drawback of the Wiener filter is needed for unlimited data, does not apply to real-time processing. To overcome this shortcoming, in the 1960s, Kalman state space model of the introduction of filtering theory, and a recursive estimation algorithm is derived, later known as the Kalman filter theory. Kalman filter based on minimum mean square error of the estimated best practices, to seek a recursive estimation algorithm, the basic idea is: the state space model of signal and noise, the first time to estimate and the present moment the observed values to update the estimated state variables, find the estimated value of the moment. It is suitable for real-time processing and computing. Platform: |
Size: 13312 |
Author:fan |
Hits:
Description: 最优估计理论书籍，国外2006年SIMON出的，非常适合学习卡尔曼滤波以及最优估计方面的知识-Optimal estimation theory books abroad in 2006 SIMON, ideal for learning the knowledge of the Kalman filter and optimal estimation Platform: |
Size: 22199296 |
Author:huangwei |
Hits:
Description: 卡尔曼滤波从与被提取信号有关的量测量中通过算法估计出所需信号。卡尔曼滤波处理有几个特点：(1)卡尔曼滤波处理的对象是随机信号；(2)被处理信号无有用和干扰之分，滤波的目的是要估计出所有被处理信号；确切的说卡尔曼滤波应称作最优估计理论；就实现形式而言，卡尔曼滤波器实质上是一套由数字计算机实现的递推算法。量测量可看作卡尔曼滤波器的输入，估计值可看作输出-Kalman filtering from the measurement of the quantity relating to the signal is extracted by the algorithm to estimate the desired signal. Kalman filtering process has several characteristics: (1) the object of processing of the Kalman filter is a random signal (2) no useful signal and the interference of the points to be processed, the purpose of the filter is to be estimated for all the signal to be processed exact Kalman filter called optimal estimation theory terms of the forms of the Kalman filter is essentially a digital computer recursive algorithm. Measurement can be regarded as the input for the Kalman filter, it is estimated that the output value can be regarded as Platform: |
Size: 701440 |
Author:郑永钊 |
Hits:
Description: 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 state observers used in control theory. Its principal advantage is that it does not require a detailed system model. Platform: |
Size: 2048 |
Author:Karthi |
Hits:
Description: 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 tend to be more precise than those based on a single measurement alone. More formally, the Kalman filter operates recursively on streams of noisy input data to produce a statistically optimal estimate of the underlying system state. The filter is named for Rudolf (Rudy) E. Kálmán, one of the primary developers of its theory.-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 tend to be more precise than those based on a single measurement alone. More formally, the Kalman filter operates recursively on streams of noisy input data to produce a statistically optimal estimate of the underlying system state. The filter is named for Rudolf (Rudy) E. Kálmán, one of the primary developers of its theory.
Platform: |
Size: 19456 |
Author:joe33 |
Hits:
Description: 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 evidence theory data fusion, Virtual power wireless sensor network coverage, Including the generalized cross-correlation function GCC time delay estimation. Platform: |
Size: 8192 |
Author:ndqnpu |
Hits:
Description: 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 data fusion, Using MATLAB compressed sensing. Platform: |
Size: 8192 |
Author:phmmax |
Hits:
Description: 最优状态估计与系统辨识 出版社：西北工业大学出版社 作者：王志贤 本书系统地阐述了最优状态估计与系统辨识的基本概念、基本理论和基本方法。全书共分两篇14章：第一篇为最优状态估计，分别介绍了最优估计的基本概念、线性系统的卡尔曼滤波、最优线性平滑、卡尔曼滤波的稳定性、滤波的发散及其克服方法、非线性滤波。第二篇为系统辨识，分别介绍了系统辨识的一般概念、脉冲响应法和相关函数法、最小二乘类辨识方法、极大似然法和预报误差法、时间序列模型和随机逼近法、多输入多输出性系统辨识、闭环系统辨识。附录给出了学习本课程中用到的矩阵分析等一些数学工具。 -Optimal state estimation and system identification Publisher: Northwestern University Press Author: Wang Zhixian This book describes the basic concepts and optimal state identification system, the basic theory and method of estimation. The book consists of two 14 chapters: The first chapter is the optimal state estimation, introduced the basic concepts of optimal estimation, Kalman filter for linear systems, optimal linear smoothing, divergence stability Kalman filter, and the filter which overcomes method, nonlinear filtering. The second is identification, introduced the general concept of system identification, impulse response and correlation function method, class identification method of least squares, maximum likelihood method and prediction error method, time series models and stochastic approximation method, and more input multi-output system identification, the closed-loop system identification. The appendix gives matrix analysis used in this course and some other mathematical Platform: |
Size: 6449152 |
Author:李赛 |
Hits:
Description: 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 Kalman and Bucy (1961)发表。
数据滤波是去除噪声还原真实数据的一种数据处理技术, Kalman滤波在测量方差已知的情况下能够从一系列存在测量噪声的数据中，估计动态系统的状态. 由于, 它便于计算机编程实现, 并能够对现场采集的数据进行实时的更新和处理, Kalman滤波是目前应用最为广泛的滤波方法, 在通信, 导航, 制导与控制等多领域得到了较好的应用.(Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. , one of the primary developers of its theory.) Platform: |
Size: 3072 |
Author:yxzfrank
|
Hits:
Description: 基于卡尔曼滤波对现有采样数据进行滤波，有效降低观测值的误差。卡尔曼滤波是一种时域方法，它把状态空间的概念引入随机估计理论，用状态方程、观测方程和噪声激励递推估计测量噪声，便于实现实时应用。(The existing sampled data is filtered based on Kalman filter, which can effectively reduce the error of the observed value. Kalman filtering is a time domain method. It introduces the concept of state space into the theory of stochastic estimation, and uses state equation, observation equation and noise excitation to estimate noise. It is easy for real-time applications.) Platform: |
Size: 1024 |
Author:会飞的鱼鱼
|
Hits:
Search - kalman filter estimation theory