用C++语言实现的策略模式框架，已编译通过，可以根据自己的实际需要增加函数和类。

鱼书《深度学习入门基于python的理论与实现》代码

卡尔曼滤波和角度测定

building machine learning system with python 实现代码前六章

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

卡尔曼滤波应用，值得一看哈，介绍的很详细，希望大家喜欢

openmv卡尔曼滤波