十分钟入门VB上位机编程，轻松学上位机。供大家学习参考。

AEKF_SOC_Estimation函数使用二阶RC等效电路模型（ECM）和自适应扩展卡尔曼滤波器（AEKF）估计电池的端电压（Vt）和荷电状态（SOC）。

永磁同步电机pmsm无感foc控制，观测器采用扩展卡尔曼滤波器ekf，代码运行无错误，支持无感启动，代码移植性强，可以移植到国产mcu上.

项目中包括锂电池模型建立、参数辨识与验证、SOC估算采用扩展卡尔曼滤波(EKF)，使用了两种方式实现： 1. Simulinks(EKF only) 2. 脚本(包含EKF和UKF) 模型的输入包括电流和电压来自于HPPC（混合脉冲功率特性）测试的电池数据 脚本文件可以仿真在BBDST(北京公交车动态街道测试)工况和带有观测噪声的恒流工况下的锂离子电池放电过程，利用EKF UKF方法估算电池荷电状态。

西门子200smart换热站程序西门子200smart换热站程序 有 变量表 源程序代码 CAD图纸 威伦屏 程序

卡尔曼滤波和角度测定

本资源是基于Java的Kalman滤波算法，可以作为一种性能较为优良的滤波器，滤去极端值。本资源可以直接将SRC文件夹中的两个子文件夹复制并使用。

卡尔曼滤波在雷达目标跟踪中的应用 matlab程序 卡尔曼滤波在雷达目标跟踪中的应用 matlab程序 卡尔曼滤波在雷达目标跟踪中的应用 matlab程序 卡尔曼滤波在雷达目标跟踪中的应用 matlab程序

1 Introduction 1 1.1 Chapter Focus, 1 1.2 On Kalman Filtering, 1 1.3 On Optimal Estimation Methods, 6 1.4 Common Notation, 28 1.5 Summary, 30 Problems, 31 References, 34 2 Linear Dynamic Systems 37 2.1 Chapter Focus, 37 2.2 Deterministic Dynamic System Models, 42 2.3 Continuous Linear Systems and their Solutions, 47 2.4 Discrete Linear Systems and their Solutions, 59 2.5 Observability of Linear Dynamic System Models, 61 2.6 Summary, 66 Problems, 69 References, 3 Probability and Expectancy 73 3.1 Chapter Focus, 73 3.2 Foundations of Probability Theory, 74 3.3 Expectancy, 79 3.4 Least-Mean-Square Estimate (LMSE), 87 3.5 Transformations of Variates, 93 3.6 The Matrix Trace in Statistics, 102 3.7 Summary, 106 Problems, 107 References, 110 4 Random Processes 111 4.1 Chapter Focus, 111 4.2 Random Variables, Processes, and Sequences, 112 4.3 Statistical Properties, 114 4.4 Linear Random Process Models, 124 4.5 Shaping Filters (SF) and State Augmentation, 131 4.6 Mean and Covariance Propagation, 135 4.7 Relationships Between Model Parameters, 145 4.8 Orthogonality Principle, 153 4.9 Summary, 157 Problems, 159 References, 167 5 Linear Optimal Filters and Predictors 169 5.1 Chapter Focus, 169 5.2 Kalman Filter, 172 5.3 Kalman–Bucy Filter, 197 5.4 Optimal Linear Predictors, 200 5.5 Correlated Noise Sources, 200 5.6 Relationships Between Kalman and Wiener Filters, 201 5.7 Quadratic Loss Functions, 202 5.8 Matrix Riccati Differential Equation, 204 5.9 Matrix Riccati Equation in Discrete Time, 219 5.10 Model Equations for Transformed State Variables, 223 5.11 Sample Applications, 224 5.12 Summary, 228 Problems, 232 References, 235 6 Optimal Smoothers 239 6.1 Chapter Focus, 239 6.2 Fixed-Interval Smoothing, 244 6.3 Fixed-Lag Smoothing, 256 6.4 Fixed-Point Smoothing, 268 7 Implementation Methods 281 7.1 Chapter Focus, 281 7.2 Computer Roundoff, 283 7.3 Effects of Roundoff Errors on Kalman Filters, 288 7.4 Factorization Methods for “Square-Root” Filtering, 294 7.5 “Square-Root” and UD Filters, 318 7.6 SigmaRho Filtering, 330 7.7 Other Implementation Methods, 346 7.8 Summary, 358 Problems, 360 References, 363 8 Nonlinear Approximations 367 8.1 Chapter Focus, 367 8.2 The Affine Kalman Filter, 370 8.3 Linear Approximations of Nonlinear Models, 372 8.4 Sample-and-Propagate Methods, 398 8.5 Unscented Kalman Filters (UKF), 404 8.6 Truly Nonlinear Estimation, 417 8.7 Summary, 419 Problems, 420 References, 423 9 Practical Considerations 427 9.1 Chapter Focus, 427 9.2 Diagnostic Statistics and Heuristics, 428 9.3 Prefiltering and Data Rejection Methods, 457 9.4 Stability of Kalman Filters, 460 9.5 Suboptimal and Reduced-Order Filters, 461 9.6 Schmidt–Kalman Filtering, 471 9.7 Memory, Throughput, and Wordlength Requirements, 478 9.8 Ways to Reduce Computational Requirements, 486 9.9 Error Budgets and Sensitivity Analysis, 491 9.10 Optimizing Measurement Selection Policies, 495 9.11 Summary, 501 Problems, 501 References, 502 10 Applications to Navigation 503 10.1 Chapter Focus, 503 10.2 Navigation Overview, 504

基于一阶RC模型，电池带遗忘因子递推最小二乘法+扩展卡尔曼滤波算法（FFRLS+ EKF），参数与SOC的在线联合估计，matlab程序