4.67 (84 reviews)
☑ 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
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!
Welcome to the Course
Setting Up Python
What is Data Fusion
How does Sensor Fusion Work
Probability Density Functions
Distribution Statistical Properties
Uniform Probability Distribution
Gaussian Probability Distribution
Linear Transformation of Gaussian Distribution
Multiple Random Variables
Multivariate Gaussian Distribution
Linear Transformation of Uncertainities
Probability Notes and Summary
State Space Representation
Differential Equations and State Space
Continuous and Discrete Time
Mathematical Models of Dynamic Systems
Continuous to Discrete Model Conversions
Simulation of Models
Python Simulation Exercise
Dynamic System Notes and Summary
Least Squares Estimation
Estimation of a Constant
Estimation of a Constant Vector
Weighted Least Squares
Weighted Least Squares
Recursive Least Squares
Recursive Least Squares
Least Squares Estimation Summary
Linear Kalman Filter
What is the Kalman Filter
Types of Kalman Filters
How Does the Linear Kalman Filter Work
2D Tracker Example Problem Overview
2D Tracker Process Model
Kalman Filter Prediction Step
2D Tracker Prediction Step
2D Tracker Prediction Step Explaination
Kalman Filter Update Step
2D Tracker Update Step
2D Tracker Update Step Explaination
2D Tracker Initial Conditions
2D Tracker Initial Condition Explaination
Kalman Filter Tuning
2D Tracker Filter Tuning
Linear Kalman Filter Notes and Summary
Pendulum Estimation Problem
System and Measurement Dynamics
Kalman Filter Model Implementation
Kalman Filter Performance and Tuning
Kalman Filter Summary
This course explains the key concepts to understand the Kalman Filters design and main concepts. Well-developed examples and resources to test all exercises in Python. Basics in probability are very good explained.
Excellent course!!! Simply excellent. Very well organized, there is enough information to be able to start applying the concepts. It is great that Python is used for examples and exercises. Because of this, we can see how a simulation (in Python, or any other language) is implemented (rather than using MATLAB’S Simulink, for example, in which case the simulation would have just magically happened and we would have learned nothing), how a least squares estimation is done, how a Kalman filter is implemented (again, NOT using MATLAB’s toolboxes, and just see that they work…. and we would learn nothing…). I suffered a bit doing the 3 example exercises, but that was because I did not know Python, and/or numpy; now, that I’ve done them, I know more ?. Thank you for this great course!
Above any lecture I ever had at university on this topic. This course directly goes to the essential: clean and efficient. I recommend it.
Awesome topic.. finally someone is speaking with right content on sensor fusion. A must do course for people working with sensors, ADAS, AI,Signal processing..Waiting for new courses from him on advance topics
The descriptions given for the mathmatics and formulas were a bit too short and abstract to follow the underlying meanings. However, I do like those Python implementation/examples given in the course, which is a really nice way to get my hands on the Kalman filter.
Pros : Well prepared course material, simulation tools and assignments, which really give a good insight into practical issues and their solutions. One of the best courses I have taken on Udemy. Cons : Would have liked to have the slides as a downloadable resource, since the downloadable documents don't reference the examples. I spent a fair amount of time taking screenshots and renaming them.
You should have included more coding exercises for implementation of Kalman Filter 1D and 2D. Also have some quizzes to test the students knowledge
I would like to see more info for the mass damper example in chapter 29, specifically how you derived the F matrice for different time delta. You "say" how to do it but it yay just display the result