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卡尔曼滤波的vb源程序(现设线性时变系统的离散状态防城和观测方程)-Kalman Filter vb source (now based linear time-varying systems of discrete state Fangcheng and observation equation)
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The Kalman filter30 is a minimum-variance filter in
which time-series measurements are incorporated recursively
into estimates of state variables it is the
optimal, Bayesian least-squares estimator for linear
dynamic systems.-dd
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卡尔曼于1960年提出了离散系统线性滤波的递推求解方法即卡尔曼滤波算法。该滤波算法是基于线性最小平方法的、进行有效递推计算的一组数学方程式,算法功能强大,支持对过去、现在和将来状态的估算。-Kalman in 1960 proposed a linear discrete-time systems to solve recursive filtering methods for the Kalman filter. The filtering algorithm is based on the
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卡尔曼于1960年提出了离散系统线性滤波的递推求解方法即卡尔曼滤波算法。该滤波算法是基于线性最小平方法的、进行有效递推计算的一组数学方程式,算法功能强大,支持对过去、现在和将来状态的估算。-Kalman in 1960 proposed a linear discrete-time systems to solve recursive filtering methods for the Kalman filter. The filtering algorithm is based on the
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THIS PROGRAM IS FOR IMPLEMENTATION OF DISCRETE TIME PROCESS UNSCENTED KALMAN FILTER FOR GAUSSIAN AND LINEAR STOCHASTIC DIFFERENCE EQUATION.
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对于扩展卡尔曼滤波在非线性系统中由于线性化过程引入了线性化误差，从而导致滤波器性能下降甚至发散造成滤波发散的情况-For the extended Kalman filter to nonlinear systems of linear process because of the introduction of the linearization error, leading to filter divergence of performance degradation and even f
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This paper presents a method that combines
colour and motion information to track pedestrians in video sequences captured by a fixed camera. Pedestrians are firstly
detected using the human detector proposed by Dalal and Triggs which involves com
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960年，卡尔曼发表了他著名的用递归方法解决离散数据线性滤波
问题的论文。从那以后，得益于数字计算技术的进步，卡尔曼滤波器
已成为推广研究和应用的主题，尤其是在自主或协助导航领域。-960, Kalman published his famous paper describing a recursive solution to the problem of discrete data linear filtering paper. Since then, thanks to advanc
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kalman filter
This scr ipt implements the linear filter and shows its performance
On a second order under-damped LTI systems
In the first part, a noisy model with two state variables is simulated and
in the second part, kalman filtering is
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卡尔曼滤波是一种数据处理方法，它是一种线性最小方差无偏估计准则，基于系统
状态估计和当前观测，通过引入状态空间而获得的新的状态估计.本篇论文陈述了卡尔曼滤
波的基本思路和算法；并通过仿真，显示卡尔曼滤波的功能，以及如何用它来跟踪方向确定、速度恒定的飞行器。-Kalman filter is a data processing method, which is a linear minimum variance unbiased estimation criteria, based on
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线性卡尔曼滤波的基本知识介绍，适合初学者学习，通俗易懂，希望对有需要的人有所帮助-Basic knowledge of linear Kalman filter, suitable for beginners to learn, easy to understand, and I hope to help people in need
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Kalman filtering with state constraints:
a survey of linear and nonlinear algorithms
D. Simon.
in this file introduced nem kalman filter and can be used for any application.
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现有的贪婪迭代类压缩感知重构算法均基于最小二乘对信号进行波形估计，未考虑到可能将量测噪声引入信号估计的情况．针对以上不足，提出了一种基于线性Kalman滤波的压缩感知弱匹配去噪重构算法-Existing greedy iterative reconstruction algorithms class compressed sensing the signal waveform based on least squares estimation does not take into account
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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 lin
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介绍了Kalman滤波器是一种线性的离散时间有限维系统，对kalman滤波算法公式进行了详细的推导-It introduced the Kalman filter is a finite-dimensional linear discrete-time system, kalman filtering algorithm formula derived in detail
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Various kalman filter design, allan FOG output error variance analysis, Jacobi iteration for solving linear equations class-based.
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Linear Quadratic Gaussian (LQR) controller and Kalman Filter (KF) both are robust when acting separately, but this property is lost if using them together.
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卡尔曼滤波原理及应用—MATLAB仿真主要介绍数字信号处理中的卡尔曼（Kalman）滤波算法及在相关领域应用。全书共7章。第1章为绪论。第2章介绍MATLAB算法仿真的编程基础。第3章介绍线性Kalman滤波。第4章讨论扩展Kalman滤波，并介绍其在目标跟踪和制导领域的应用和算法仿真。第5章介绍UKF滤波算法，同时也给出其应用领域内的算法仿真实例。第6章介绍了交互多模型Kalman滤波算法。第7章介绍Simulink环境下，如何通过模块库和S函数构建Kalman滤波器，并给出了系统是线性和非线
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