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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-fusion-with-linear-kalman-filter/
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课程名称：线性卡尔曼滤波的数据融合 课程概述：本课程致力于教授数据融合和卡尔曼滤波的知识。卡尔曼滤波被誉为估计和数据融合理论历史上最伟大的发现之一，甚至是20世纪最伟大的工程发现之一。它使人类能够完成许多以前无法实现的任务，并在复杂动态系统的控制中得到了广泛应用，如汽车、飞机、船舶和航天器等。该理论不仅在工程和制造业中得到应用，还涉及化学、生物、金融、经济等多个领域。 为什么要专注于数据融合和卡尔曼滤波？数据融合是一种在现代技术中广泛应用的工具，几乎所有涉及传感、测量或自动化的现代技术都在使用这一工具。而卡尔曼滤波是数据融合中最常用的方法之一。了解这一过程将使您更容易理解更复杂的方法。对于初学者来说，理解滤波器的工作原理以及如何实际应用这些概念相对困难。我们还将提供评估和调优卡尔曼滤波器的技巧和结构，帮助您避免在调试中浪费时间，成为该领域的专家！ 您将学习的内容包括： - 从基础概率和随机变量入手，了解不确定性的概率表达。 - 将微分系统转换为状态空间表示。 - 模拟和描述状态空间动态系统。 - 使用最小二乘估计解决估计问题。 - 使用线性卡尔曼滤波解决最优估计问题。 - 一般性地推导卡尔曼滤波器的系统矩阵。 - 优化调节线性卡尔曼滤波器以获得最佳性能。 - 在Python中实现线性卡尔曼滤波器。 适合对象： - 大学学生或独立学习者。 - 从事相关工作的工程师和科学家。 - 希望复习与数据融合和卡尔曼滤波相关的数学理论和技能的工程专业人士。 - 希望理解数据融合基本概念以便实施或支持数据融合代码开发的软件开发者。 - 已经精通数学“理论”的人，想学习如何将理论实施到代码中的人。 如果您觉得这门课程对您有帮助，请观看课程介绍视频和免费样本，以便了解课程内容。如果课程不合适，提供退款保证。期待在课程中与您见面！Steve
You need to learn know Data Fusion and Kalman Filtering!The Kalman filter is one of the greatest discoveries in the history of estimation and data fusion theory, and perhaps one of the greatest engineering discoveries in the twentieth century. It has enabled mankind to do and build many things which could not be possible otherwise. It has immediate application in control of complex dynamic systems such as cars, aircraft, ships and spacecraft.These concepts are used extensively in engineering and manufacturing but they are also used in many other areas such as chemistry, biology, finance, economics, and so on. Why focus on Data Fusion and Kalman FilteringData Fusion is an amazing tool that is used pretty much in every modern piece of technology that involves any kind of sensing, measurement or automation.The Kalman Filter is one of the most widely used methods for data fusion. By understanding this process you will more easily understand more complicated methods.Difficult for beginners to comprehend how the filter works and how to apply the concepts in practice.Evaluating and tuning the Kalman Filter for best performance can be a bit of a 'black art', we will give you tips and a structure so you know how to do this yourself.So you don't waste time trying to solve or debug problems that would be easily avoided with this knowledge! Become a Subject Matter Expert!What you will learn:You will learn the theory from ground up, so you can completely understand how it works and the implications things have on the end result. You will also learn practical implementation of the techniques, so you know how to put the theory into practice.We will cover:Basic Probability and Random VariablesDynamic Systems and State Space RepresentationsLeast Squares EstimationLinear Kalman FilteringCovers theory, implementation, use casesTheory explanation and analysis using Python and SimulationsBy the end of this course you will know:How to probabilistically express uncertainty using probability distributionsHow to convert differential systems into a state space representationHow to simulate and describe state space dynamic systemsHow to use Least Squares Estimation to solve estimation problemsHow to use the Linear Kalman Filter to solve optimal estimation problemsHow to derive the system matrices for the Kalman Filter in general for any problemHow to optimally tune the Linear Kalman Filter for best performanceHow to implement the Linear Kalman Filter in PythonWho is this course for:University students or independent learners.Working Engineers and Scientists.Engineering professionals who wants to brush up on the math theory and skills related to Data Fusion and Kalman filtering.Software Developers who wish to understand the basic concepts behind data fusion to aid in implementation or support of developing data fusion code.Anyone already proficient with the math "in theory" and want to learn how to implement the theory in code.So what are you waiting for??Watch the course instruction video and free samples so that you can get an idea of what the course is like. If you think this course will help you then sign up, money back guarantee if this course is not right for you.I hope to see you soon in the course!Steve
