该资源是卡尔曼滤波（Kalman Filter）原理，内含实例代码，看了众多文章后，自己的理解。实例一个鼠标跟踪程序。

Introduction to Random Signals and Applied Kalman Filtering,4版代码，好东西 学习信号处理和卡尔曼滤波经典教材的代码

Robert Grover Brown , Patrick Y. C. Hwang 经典的卡尔曼滤波书籍，清晰第4版。 In this updated edition the main thrust is on applied Kalman filtering. Chapters 1-3 provide a minimal background in random process theory and the response of linear systems to random inputs. The following chapter is devoted to Wiener filtering and the remainder of the text deals with various facets of Kalman filtering with emphasis on applications. Starred problems at the end of each chapter are computer exercises. The authors believe that programming the equations and analyzing the results of specific examples is the best way to obtain the insight that is essential in engineering work.

This is a practical guide to building Kalman filters that shows how the filtering equations can be applied to real-life problems. Numerous examples are presented in detail, showing the many ways in which Kalman filters can be designed. Computer code written in FORTRAN, MATLAB®, and True BASIC accompanies all of the examples so that the interested reader can verify concepts and explore issues beyond the scope of the text. In certain instances, the authors intentionally introduce mistakes to the initial filter designs to show the reader what happens when the filter is not working properly. The text carefully sets up a problem before the Kalman filter is actually formulated, to give the reader an intuitive feel for the problem being addressed. Because real problems are seldom presented as differential equations, and usually do not have unique solutions, the authors illustrate several different filtering approaches. Readers will gain experience in software and performance tradeoffs for determining the best filtering approach. The material that has been added to this edition is in response to questions and feedback from readers. The third edition has three new chapters on unusual topics related to Kalman filtering and other filtering techniques based on the method of least squares.Chapter 17 presents a type of filter known as the fixed or finite memory filter, which only remembers a finite number of measurements from the past. Chapter 18 shows how the chain rule from calculus can be used for filter initialization or to avoid filtering altogether. A realistic three-dimensional GPS example is used to illustrate the chain-rule method for filter initialization. Finally, Chapter 19 shows how a bank of linear sine-wave Kalman filters, each one tuned to a different sine-wave frequency, can be used to estimate the actual frequency of noisy sinusoidal measurements and obtain estimates of the states of the sine wave when the measurement noise is low.

 文中针对水下自主航行器提出了一种新型的基于捷联惯导（SINS）和GPS的组合导航系统设计方案。该方案以捷联惯导作为主系统，同时利用GPS重调捷联惯导系统，建立了该组合导航系统的卡尔曼滤波模型，设计了输出校正间接法的卡尔曼滤波算法和Sage-husa自适应卡尔曼滤波算法。仿真结果表明由于GPS位置和速度信息的引入，一定程度上克服了捷联惯导系统误差状态发散现象，提高了导航精度。同时通过两种算法的对比，Sage-husa自适应卡尔曼滤波算法则具有更高的滤波精度和稳定性，能够更好的满足长时间远距离导航的要求。

LSTM_Kalman预测温度代码，可直接运行，内有程序使用说明。

最后更改 新许可证Apache 2.0代替GPLv3 添加了新的参数“批处理大小”-在多个连续帧上同时检测。 它可以在功能强大的GPU上提高处理速度。 适用于Darknet和TensorRT后端，但可能会增加一些延迟 新影片！ 使用YOLO v4进行车速计算（感谢 ） YOLO v4迈向ADAS的第一步 多目标（多个对象）跟踪器 1.可以使用具有不同的detectorType值的功能创建对象检测器： 1.1。 根据背景扣除：内置Vibe（跟踪：: Motion_VIBE），SuBSENSE（跟踪：: Motion_SuBSENSE）和LOBSTER（跟踪：: Motion_LOBSTER）； 来自MOG2（tracking :: Motion_MOG2）； MOG（跟踪:: Motion_MOG），GMG（跟踪:: Motion_GMG），并从CNT（跟踪:: Motion_CNT

基于天基滤波matlab

工业实时数据滤波算法 工业实时数据滤波算法

Sensor fusion using Dempster-Shafer theory II - static weighting and Kalman filter-like dynamic weighting.pdf