Data Fusion with Linear Kalman Filter

卡尔曼滤波器是估计和数据融合理论史上最伟大的发现之一，也许是二十世纪最伟大的工程发现之一。它使人类能够做和建造许多其他方式无法实现的事情。它直接应用于控制复杂的动态系统，如汽车、飞机、船舶和航天器。

这些概念广泛用于工程和制造，但也用于许多其他领域，如化学、生物学、金融、经济学等。

为什么关注数据融合和卡尔曼滤波

数据融合是一个了不起的工具，几乎用于涉及任何类型的传感、测量或自动化的每一项现代技术。

卡尔曼滤波器是应用最广泛的数据融合方法之一。通过理解这个过程，你会更容易理解更复杂的方法。

初学者很难理解过滤器的工作原理以及如何在实践中应用这些概念。

评估和调整卡尔曼滤波器以获得最佳性能可能有点像“黑色艺术”，我们将为您提供提示和结构，以便您知道如何自己做到这一点。

因此，您不必浪费时间尝试解决或调试使用这些知识可以轻松避免的问题！成为主题专家！

你将学到什么：

您将从头开始学习该理论，因此您可以完全了解它的工作原理以及事物对最终结果的影响。您还将学习这些技术的实际实施，因此您知道如何将理论付诸实践。

我们将涵盖：

基本概率和随机变量

动态系统和状态空间表示

最小二乘估计

线性卡尔曼滤波

涵盖理论、实现、用例

使用Python和模拟进行理论解释和分析

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.70 GB | Duration: 5h 25m

What you’ll learn
How to probabilistically express uncertainty using probability distributions
How to convert differential systems into a state space representation
How to simulate and describe state space dynamic systems
How to use Least Squares Estimation to solve estimation problems
How to use the Linear Kalman Filter to solve optimal estimation problems
How to derive the system matrices for the Kalman Filter in general for any problem
How to optimally tune the Linear Kalman Filter for best performance
How to implement the Linear Kalman Filter in Python

Requirements
Basic understanding of linear algebra
Basic Python Programming Experience

Description
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 Filtering

Data 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 Variables

Dynamic Systems and State Space Representations

Least Squares Estimation

Linear Kalman Filtering

Covers theory, implementation, use cases

Theory explanation and analysis using Python and Simulations

By the end of this course you will know:

How to probabilistically express uncertainty using probability distributions

How to convert differential systems into a state space representation

How to simulate and describe state space dynamic systems

How to use Least Squares Estimation to solve estimation problems

How to use the Linear Kalman Filter to solve optimal estimation problems

How to derive the system matrices for the Kalman Filter in general for any problem

How to optimally tune the Linear Kalman Filter for best performance

How to implement the Linear Kalman Filter in Python

Who 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

Who this course is 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