Description: 卡尔曼滤波在数学上是一种统计估算方法，通过处理一系列带有误差的实际量测数据而得到的物理参数的最佳估算。
例如在气象应用上，根据滤波的基本思想，利用前一时刻预报误差的反馈信息及时修正预报方程，以提高下一时刻预报精度。
作温度预报一般只需要连续两个月的资料即可建立方程和递推关系。
-Kalman filter in mathematics is a statistical estimation methods, By a series of errors with the actual measurement data and the physical parameters of the best estimate. For example, in meteorological applications, in accordance with the basic idea filtering, Before the use of a forecast error of the feedback information in a timely manner that prediction equation, the next moment to improve prediction accuracy. Predict the temperature usually require only two consecutive months of the establishment of equations and can be recursive relationship. Platform: |
Size: 817 |
Author:于军相 |
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Description: 卡尔曼滤波在数学上是一种统计估算方法，通过处理一系列带有误差的实际量测数据而得到的物理参数的最佳估算。
例如在气象应用上，根据滤波的基本思想，利用前一时刻预报误差的反馈信息及时修正预报方程，以提高下一时刻预报精度。
作温度预报一般只需要连续两个月的资料即可建立方程和递推关系。
-Kalman filter in mathematics is a statistical estimation methods, By a series of errors with the actual measurement data and the physical parameters of the best estimate. For example, in meteorological applications, in accordance with the basic idea filtering, Before the use of a forecast error of the feedback information in a timely manner that prediction equation, the next moment to improve prediction accuracy. Predict the temperature usually require only two consecutive months of the establishment of equations and can be recursive relationship. Platform: |
Size: 1024 |
Author:于军相 |
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Description: 用一观测器从t=1秒开始对一个运动目标的距离进行连续地跟踪测量，假设观测的间隔为一秒钟，雷达到运动目标之间的距离为S(t)（1） 统计特性的初值为
（2）观测误差是与和均不相关的白噪声序列，并且有
（3）观测数据存放在附加的文件中（单位：m）。
要求：分析上述对象，建立系统模型，构造卡尔曼滤波器，编程计算，求：
（1） 距离S(t)的最佳估计及估计误差，
（2） 距离S(t-5)的最佳平滑及估计误差，
（3） 距离S(t+5)的最佳预测及估计误差，
（4） 对结果进行分析讨论。
-By one observer from the t = 1 PST on a moving target tracking for distance measurement, assuming that the observation interval is one second, the radar that the distance between the moving target for the S (t) (1) the statistical characteristics of the initial condition (2) observational error is not associated with white noise sequence, and (3) observational data stored in the attached document (unit: m). Requirements: Analysis of the above-mentioned object, the establishment of the system model, constructed Kalman filter, programming terms, seeking: (1) distance from S (t) the best estimate and the estimation error, (2) distance from S (t-5) the most good smoothing and estimation error, (3) distance from S (t+ 5) the best prediction and estimation error, (4) the results analyzed and discussed. Platform: |
Size: 2048 |
Author:裴海波 |
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Description: 在对船用陀螺漂移数据建立时间序列模型的基础上，采用卡尔曼滤波算法对捷联陀螺漂移数
据进行了处理，以提高陀螺静态漂移误差系数的估计精度，并把得到的陀螺漂移误差模型实时补偿到捷联系统中，得到了满意的效果。-Marine gyro drift in the data time series model based on the Kalman filter algorithm using inertial gyro drift data were processed to enhance the gyro drift error coefficient of static estimation accuracy, and to get the gyro drift error model for real-time compensation to the inertial system with satisfactory results. Platform: |
Size: 204800 |
Author:我爱 |
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Description: INTER-CARRIER INTERFERENCE CANCELLATION FOR OFDM SYSTEMS:
Orthogonal Frequency Division Multiplexing (OFDM) is an emerging multi-carrier modulation scheme, which has been adopted for several wireless standards such as IEEE 802.11a and HiperLAN2. A well-known problem of OFDM is its sensitivity to frequency offset between the transmitted and received carrier frequencies. This frequency offset introduces inter-carrier interference (ICI) in the OFDM symbol. This project investigates three methods for combating the effects of ICI: ICI self-cancellation (SC), maximum likelihood (ML) estimation, and extended Kalman filter (EKF) method. These three methods are compared in terms of bit error rate performance, bandwidth efficiency, and computational complexity. Through simulations, it is shown that the three techniques are effective in mitigating the effects of ICI. For high values of the frequency offset and for higher order modulation schemes, the ML and EKF methods perform better than the SC method. Platform: |
Size: 390144 |
Author:redami |
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Description: matlab程序很详细,主要是kalman滤波部分程序，轨迹估计，以及误差估计等最小线性误差估计-matlab program in great detail, the main part of the program is the kalman filter, trajectory estimation, and error estimation of the minimum linear error estimation Platform: |
Size: 13312 |
Author:大宝 |
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Description: An implementation of Unscented Kalman Filter for nonlinear state estimation.-Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system.
The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF.
For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and a Platform: |
Size: 2048 |
Author:DT丿灬雪狼 |
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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 |
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Description: Extended Kalman Filter Tracking Object in 3-D-Assume that we want to track an object moving in 3-D space with constant velocity. Our instruments observe bearing, range and high(cylindrical coordinates). However, of an interest are rectangular coordinates. Since transformation is non-linear this requires use of extended Kalman filter.
Because transformation is non-linear between X,Y and Range,Bearing and linear between Z and high(Z is height), this problems serves as a good comparason of how well extended Kalman filter can perform. By comparing its linear estimation error in Z to non-linear estimations in X and Y, we can judge how non-familiarities effect estimation. Platform: |
Size: 3072 |
Author:刘建国 |
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Description: 采用卡尔曼滤波器对系统的俯仰角、
滚转角和航向角的误差进行最优估计；设计数据融合的判别准则，并根据判据的判断结果调整卡尔曼
滤波器中的量测信息，使系统可用于小型无人机的定高自主飞行-Kalman filter system pitch angle, roll angle and yaw angle error optimal estimation design data fusion criterion, and adjust according to the criterion of judging the results of measurement in the Kalman filter, making the system set high for small UAVs autonomous flight Platform: |
Size: 960512 |
Author:华氏 |
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Description: 基于非线性动力系统的无迹卡尔曼滤波matlab程序-onlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems.
The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation.
However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system.
The Unscented Kalman Filter (UKF) is a novel development in the field Platform: |
Size: 8192 |
Author:窦贤明 |
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Description: Altitude location for UAV by using federated filter
is discussed, the fourth structure is selected, because its two
sub-filters involving altitude sensor and the difference Global
Positioning System (d-GPS) respectively are fully isolated from
each other and thus make the federated filter more fault-tolerant
and better in real-time performance. Data fusion based on this
federated filter was simulated. When the d-GPS is working
normally and simulation results show that values estimated by
data fusion base on federated filter are very close to real values
and the variance of federated filter converges to 10m. When the
electromagnetic environment is very bad, as is usually the case in
war zone, the d-GPS is silent for a long time, variance based on
federated filter converges to 13m, bigger than the 10m.
Compared with previous paper variance based on Kalman
converges to 15m, higher than the 13m. Federated filter can give
much more accurate estimation than Kalman filter. Platform: |
Size: 328704 |
Author:Clovis |
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Description: 为提高天体参数测量和计算的精度，尽可能地减小误差。应用卡尔曼滤波稳健估计的
原理与方法，在自适应卡尔曼滤波器的基础上进行加权运算。计算机仿真结果表明，同其它
卡尔曼滤波方法相比较，该方法较好地抑制了参数计算中的误差，提高了天文导航定位数据
的可信度。-In order to improve the accuracy of the measurement and calculation of celestial parameter, as much as possible to reduce the error. Robust estimation principle and method of application of Kalman filtering, adaptive Kalman filter based on a weighted calculation. The computer simulation results show that, compared with other Kalman filtering method, the method is better suppression parameter calculation error and improve the credibility of astronomical navigation and positioning data. Platform: |
Size: 159744 |
Author:长沙 |
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Description: 卡尔曼滤波器是一个对动态系统的状态序列进行线性最小误差估计的算法，一般用于线性系统。一般在运动跟踪领域中摄像机相对于目标物体运动有时属于非线性系统，但由于在一般运动跟踪问题中图像采集时间间隔较短，可近似将单位时间内目标在图像中的运动看作匀速运动，采用卡尔曼滤波器可以实现对目标运动参数的估计。-Kalman filter is a state sequence of linear dynamic systems smallest error estimation algorithm for linear systems in general. Usually in the field of motion tracking camera motion with respect to the target object may belong to nonlinear systems, but in general due to motion tracking problems shorter image acquisition time interval, a unit of time can be approximated to the target motion in the image is regarded as uniform motion, Kalman filter can achieve the target motion parameter estimation. Platform: |
Size: 3072 |
Author:anly |
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Description: In statistics and signal processing, a minimum mean square error (MMSE) estimator is an estimation method which minimizes the mean square error (MSE) of the fitted values of a dependent variable, which is a common measure of estimator quality. In the Bayesian setting, the term MMSE more specifically refers to estimation in a Bayesian setting with quadratic cost function. In such case, the MMSE estimator is given by the posterior mean of the parameter to be estimated. Since the posterior mean is cumbersome to calculate, the form of the MMSE estimator is usually constrained to be within a certain class of functions. Linear MMSE estimators are a popular choice since they are easy to use, calculate, and very versatile. It has given rise to many popular estimators such as the Wiener-Kolmogorov filter and Kalman filter Platform: |
Size: 1024 |
Author:nagendra |
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Description: In statistics and signal processing, a minimum mean square error (MMSE) estimator is an estimation method which minimizes the mean square error (MSE) of the fitted values of a dependent variable, which is a common measure of estimator quality. In the Bayesian setting, the term MMSE more specifically refers to estimation in a Bayesian setting with quadratic cost function. In such case, the MMSE estimator is given by the posterior mean of the parameter to be estimated. Since the posterior mean is cumbersome to calculate, the form of the MMSE estimator is usually constrained to be within a certain class of functions. Linear MMSE estimators are a popular choice since they are easy to use, calculate, and very versatile. It has given rise to many popular estimators such as the Wiener-Kolmogorov filter and Kalman filter. Platform: |
Size: 1024 |
Author:Said |
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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 |
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Description: 基于状态估计的kalman滤波在位移速度追踪上的应用，以平均估计误差为性能指标-Based on state estimation kalman filter in the displacement speed track, with an average estimation error performance index Platform: |
Size: 1024 |
Author:张露 |
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Description: 考虑雨衰 阴影 和多径影响，光纤陀螺输出误差的allan方差分析，对于初学者具有参考意义，现代信号处理中谱估计在matlab中的使用，毕业设计有用，各种kalman滤波器的设计。- Consider shadow rain attenuation and multipath effects allan FOG output error variance analysis, For beginners with a reference value, Modern signal processing used in the spectral estimation in matlab, Graduation useful Various kalman filter design. Platform: |
Size: 6144 |
Author:pqhrza |
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Description: 一种综合多种算法的车辆检测和追踪方法，运行时间较长，但效果很棒(We implement a system for vehicle detection and
tracking from traffic video using Gaussian mixture models and
Bayesian estimation. In particular, the system provides robust
foreground segmentation of moving vehicles through a K-means
clustering approximation as well as vehicle tracking correspon-
dence between frames by correlating Kalman and particle filter
prediction updates to current observations through the solution
of the assignment problem. In addition, we conduct performance
and accuracy benchmarks that show about a 90 percent reduc-
tion in runtime at the expense of reducing the robustness of
the mixture model classification and about a 30 percent and 45
percent reduction in accumulated error of the Kalman filter and
particle filter respectively as compared to a system without any
prediction.) Platform: |
Size: 6215680 |
Author:O(∩_∩)O哈哈噢
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Search - error estimation in kalman filter