Udemy – Advanced Kalman Filtering and Sensor Fusion 2021-7

Udemy – Advanced Kalman Filtering and Sensor Fusion 2021-7 Downloadly IRSpace

Apr 17, 2025 - 13:48 Updated: Aug 17, 2025 - 13:05

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Udemy – Advanced Kalman Filtering and Sensor Fusion 2021-7

Advanced Kalman Filtering and Sensor Fusion, You need to learn know Sensor Fusion and Kalman Filtering! Learn how to use these concepts and implement them with a focus on autonomous vehicles in this course. 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. 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. In this course you will work with a C++ simulation that leads you through the implementation of various Kalman filtering methods for autonomous vehicles. At the end of the course, the Capstone project is to implement the Unscented Kalman Filter and run it as it would be used in a real self-driving car or autonomous vehicle!

What you’ll learn

How to use the Linear Kalman Filter to solve linear optimal estimation problems
How to use the Extended Kalman Filter to solve non-linear estimation problems
How to use the Unscented Kalman Filter to solve non-linear estimation problems
How to fuse in measurements of multiple sensors all running at different update rates
How to tune the Kalman Filter for best performance
How to correctly initialize the Kalman Filter for robust operation
How to model sensor errors inside the Kalman Filter
How to use fault detection to remove bad sensor measurements
How to implement the above 3 Kalman Filter Variants in C++
How to implement the LKF in C++ for a 2d Tracking Problem
How to implement the EKF and UKF in C++ for an autonomous self-driving car problem

Who this course is for

University students or independent learners
Aspiring robotic or self-driving car engineers
Working Engineers and Scientists
Engineering professionals who want to brush up on the math theory and skills related to Kalman filtering and Sensor Fusion
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