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

卡尔曼与贝叶斯 Python版本 本书教你如何解决这些过滤噪声问题，设计滤波器。 使用了许多不同的算法，但它们都基于贝叶斯概率。 简单来说，贝叶斯概率根据过去的信息确定可能是真实的。

【达摩老生出品，必属精品，亲测校正，质量保证】 资源名：基于matlab卡尔曼滤波的运动目标(人体)识别追踪程序源码+图片集+毕业论文_运动目标跟踪_卡尔曼滤波_人体识别_matlab 资源类型：matlab项目全套源码 源码说明： 全部项目源码都是经过测试校正后百分百成功运行的，如果您下载后不能运行可联系我进行指导或者更换。 适合人群：新手及有一定经验的开发人员

详细介绍了卡尔曼滤波器的matlab仿真过程

针对具有网络传输延时和噪声的多车辆系统的编队问题,提出了一种基于自适应卡尔曼滤波器的协作路径跟踪控制方法.根据车辆运动学模型和给定队形及其路径参数,给出车辆协作路径跟踪控制器设计方法,将系统线性化,针对延时情况重构状态方程,用自适应卡尔曼滤波算法滤除噪声的影响,实现系统稳定控制.仿真实验证明了该方法的有效性.