Search - Optimal State Estimation

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[Other resource] hmm_new DL : 0: HMM(Hidden Markov Model)，狀態數目N=3，觀察符號數目M=2，時間長度T=3。 (a) Probability Evaluation: 給定狀態轉換機率A、狀態符號觀察機率B、和起始機率，求觀察序列出現的機率。 (b) Optimal State Sequence: 給定狀態轉換機率A、狀態符號觀察機率B、起始機率、和觀察序列，求一個狀態序列使得O出現的機率最大。 (c) Parameter Estimation: 給定狀態轉換機率A、狀態符號觀察機率B、起始機率、和觀察序列，求新的A、B、，使得O出現的機率最大。 -HMM (Hidden Markov Model), state the number of N = 3, Observation number of symbols M = 2, T = length of three. (A) Probability Evaluation : given state transition probability A, Observer status symbol probability of B, and initial probability for observation sequence in the octave. (B) Optimal State Sequence : given state transition probability A, Observer status symbol probability of B, the initial probability, and observation sequence, for a state sequence of O makes the greatest risk. (C) Parameter Estimation : given state transition probability A, Observer status symbol probability of B, the initial probability, and observation sequence, the way the A, B, and O makes the greatest risk.
Update : 2008-10-13 Size : 143.13kb Publisher : 章勝鈞
[Other resource] kalman DL : 0: Carrier-phase synchronization can be approached in a general manner by estimating the multiplicative distortion (MD) to which a baseband received signal in an RF or coherent optical transmission system is subjected. This paper presents a unified modeling and estimation of the MD in finite-alphabet digital communication systems. A simple form of MD is the camer phase exp GO) which has to be estimated and compensated for in a coherent receiver. A more general case with fading must, however, allow for amplitude as well as phase variations of the MD. We assume a state-variable model for the MD and generally obtain a nonlinear estimation problem with additional randomly-varying system parameters such as received signal power, frequency offset, and Doppler spread. An extended Kalman filter is then applied as a near-optimal solution to the adaptive MD and channel parameter estimation problem. Examples are given to show the use and some advantages of this scheme.
Update : 2008-10-13 Size : 808.2kb Publisher : 吴大亨
[VC/MFC] hmm_new DL : 0: HMM(Hidden Markov Model)，狀態數目N=3，觀察符號數目M=2，時間長度T=3。 (a) Probability Evaluation: 給定狀態轉換機率A、狀態符號觀察機率B、和起始機率，求觀察序列出現的機率。 (b) Optimal State Sequence: 給定狀態轉換機率A、狀態符號觀察機率B、起始機率、和觀察序列，求一個狀態序列使得O出現的機率最大。 (c) Parameter Estimation: 給定狀態轉換機率A、狀態符號觀察機率B、起始機率、和觀察序列，求新的A、B、，使得O出現的機率最大。 -HMM (Hidden Markov Model), state the number of N = 3, Observation number of symbols M = 2, T = length of three. (A) Probability Evaluation : given state transition probability A, Observer status symbol probability of B, and initial probability for observation sequence in the octave. (B) Optimal State Sequence : given state transition probability A, Observer status symbol probability of B, the initial probability, and observation sequence, for a state sequence of O makes the greatest risk. (C) Parameter Estimation : given state transition probability A, Observer status symbol probability of B, the initial probability, and observation sequence, the way the A, B, and O makes the greatest risk.
Update : 2025-03-07 Size : 143kb Publisher : 章勝鈞
[Other] kalman DL : 0: Carrier-phase synchronization can be approached in a general manner by estimating the multiplicative distortion (MD) to which a baseband received signal in an RF or coherent optical transmission system is subjected. This paper presents a unified modeling and estimation of the MD in finite-alphabet digital communication systems. A simple form of MD is the camer phase exp GO) which has to be estimated and compensated for in a coherent receiver. A more general case with fading must, however, allow for amplitude as well as phase variations of the MD. We assume a state-variable model for the MD and generally obtain a nonlinear estimation problem with additional randomly-varying system parameters such as received signal power, frequency offset, and Doppler spread. An extended Kalman filter is then applied as a near-optimal solution to the adaptive MD and channel parameter estimation problem. Examples are given to show the use and some advantages of this scheme.
Update : 2025-03-07 Size : 808kb Publisher : 吴大亨
[Algorithm] OSE_Code DL : 0: 《Optimal State Estimation - Kalman, H Infinity, and Nonlinear Approaches》一书的配套源码，包括了Kalman Filter、Hinf Filter、Particle Filter等的Matlab源码-《Optimal State Estimation- Kalman, H Infinity, and Nonlinear Approaches》source code，including Matlab code of Kalman Filter、Hinf Filter、Particle Filter.
Update : 2025-03-07 Size : 49kb Publisher : 张留
[matlab] Optimalstateestimationandsystemidentification DL : 0: 最优状态估计与系统辨识,讲述了如何进行参数辨识。-Optimal state estimation and system identification, described how the parameter identification.
Update : 2025-03-07 Size : 3.2mb Publisher : 王礼鹏
[Other] OptimalStateEstimation DL : 0: 老师给的关于最优估计的matlab程序，包括卡尔曼无迹卡尔曼等等的demos 和相关的pdf学习文档-Optimal State Estimation
Update : 2025-03-07 Size : 825kb Publisher : lihao
[Software Engineering] paixu DL : 0: 用于在配电网状态估计之前对一定数量的测量装置在配电网中进行最优安装。-On a number of measuring devices used for state estimation of the distribution network in the distribution network for optimal installation.
Update : 2025-03-07 Size : 1kb Publisher :
[matlab] optimal-state-estimation DL : 1: 用于matlab的最优状态估计工具，用于状态估计和参数估计-optimal state estimation for matlab
Update : 2025-03-07 Size : 56kb Publisher : 吴林军
[matlab] fcn_SR_KF DL : 0: This file compares three different versions of the Kalman filter. The Kalman filter is used for recursive parameter estimation. The Kalman filter can handle noisy measurements. The first implemented filter (fcn_KF) is the Kalman filter with standard update of the covariance matrix P. The covariance matrix reflects the uncertainties of the predictions. To improve the numerical stability Potter developed a square root update (fcn_KF_SRP) of the covariance matrix P. Another version is the square root covariance update via triangularization (fcn_KF_SRT). This file generates a model. Then the three Kalman filters perform an estimation of the model parameter. At the end the results are compared. Sources: Simon, D. (2006): Optimal state estimation Kaminski, P. (1971): Discrete Square Root Filtering: A Survey of Current Techniques Golub, G. (1996): Matrix Computations
Update : 2025-03-07 Size : 4kb Publisher : Hashem
[Other] Optimal-State-Estimation DL : 0: 状态估计领域权威书籍涉及例子的代码。涉及到卡尔曼滤波、扩展卡尔曼滤波、无迹卡尔曼滤波及粒子滤波等。-Matlab codes for the book named 《Optimal State Estimation》. These codes include Kalman filter, Extended Kalman filter, Uncented Kalman filter, and particle filter.
Update : 2025-03-07 Size : 640kb Publisher : 张强
[SCM] My-understanding-of-control DL : 0: 飞机姿态算法。从这篇文章是我尝试的飞行器姿态检测采用四元数方法,然后利用卡尔曼滤波算法,并尝试卡尔曼滤波器耦合的多个状态变量可以是一个复杂的过程,线性系统状态估计进行了简单的解耦,将最优估计的态度和最优估计陀螺漂移,通过这种方式,可以通过直观的方法来调整参数的两个部分。-Aircraft attitude algorithm.From this article is my attempt to spacecraft attitude detection by using the quaternion method, to then using kalman filter algorithm, and try to kalman filter coupling multiple state variables can be a complex process of linear system state estimation has carried on the simple decoupling, which will be the attitude of the optimal estimation and the optimal estimate gyro drift apart, in this way, can through the intuitive method to adjust the parameters of the two parts.
Update : 2025-03-07 Size : 35kb Publisher : 张文龙
[Other] Optimal-State-Estimation(m) DL : 0: 最优状态估计，包括卡尔曼滤波、H_inf、UKF-optimal state estimation
Update : 2025-03-07 Size : 100kb Publisher : youjinchuan
[source in ebook] Code DL : 0: Optimal state estimation源码-optical state estimation
Update : 2025-03-07 Size : 52kb Publisher : luoyi
[Other] OSE DL : 0: 最优状态估计，bayesian估计，kalman滤波器，particle滤波器-optimal state estimation
Update : 2025-03-07 Size : 20.69mb Publisher : yinhao
[Other] Optimalstateestimation DL : 0: 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
Update : 2025-03-07 Size : 6.15mb Publisher : 李赛
[assembly language] Optimal-State-Estimation-Kalman DL : 0: 本程序是有关粒子滤波，及其其在组合导航中的应用有关程序，可以仿真-This procedure is related to the particle filter, and its application in integrated navigation procedures, you can emulate
Update : 2025-03-07 Size : 20.64mb Publisher : 乔嘉歆
[e-language] matpower6.0b2 DL : 0: Matpower6.0b2的教育版，主要用于电力系统仿真，其中包括潮流计算、最优潮流、状态估计等。(The 6.0b2 Version of Matpower is provided in this file. It is an education version for power sytem analysis. The power flow, optimal power flow, and state estimation functions are included in the simulation.)
Update : 2025-03-07 Size : 10.56mb Publisher : Andromeda
[Other] Optimal State Estimation DL : 0: 关于最优状态估计的经典书籍，卡尔曼滤波和H无穷等方法的详细介绍，作者Dan Simon是真.大牛s(Classic book about optimal estimation and related methods, insight and detailed description by the famous professor Dan Simon)
Update : 2025-03-07 Size : 20.51mb Publisher : 轻舞肥喵
[Industry research] Particle Swarm Optimization of an Extended Kalman Filter for speed and rotor flux estimation of an induction motor drive DL : 0: A novel method based on a combination of the Extended Kalman Filter (EKF) with Particle Swarm Optimization (PSO) to estimate the speed and rotor flux of an induction motor driveis presented. The proposed method will be performed in two steps. As a first step, the covariance matrices of state noise and measurement noise will be optimized in an off-line manner by the PSO algorithm. As a second step, the optimal values of the above covariance matrices are injected in our speed-rotor flux estimation loop (on-line).Computer simulations of the speed and rotor-flux estimation have been performed in order to investigate the effectiveness of the proposed method. Simulations and comparison with genetic algorithms (GAs) show that the results are very encouraging and achieve good performances.
Update : 2019-01-08 Size : 650.15kb Publisher : pudn0507@yahoo.fr

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