Download Udemy – Advanced Kalman Filtering and Sensor Fusion 2021-7

Advanced Kalman Filtering and Sensor Fusion


Advanced Kalman Filtering and Sensor Fusion, the Advanced Kalman Filtering and Sensor Fusion training course has been published by Udemy Academy. The Kalman filter is one of the greatest discoveries in the history of estimation theory and data synthesis, and perhaps one of the greatest engineering discoveries in the 20th century. This work has enabled humans to do and build many things that were not possible otherwise. Kalman filter is directly used in controlling complex dynamic systems such as cars, airplanes, ships and spacecraft. These concepts are widely used in engineering and manufacturing but are also used in many other fields such as chemistry, biology, finance, economics, etc.

You will learn this theory from the ground up, so you can fully understand how it works and what implications it has on the bottom line. You also learn the practical implementation of techniques so you know how to put theory into practice. In this course you will work with a C++ simulation that guides you to implement various Kalman filtering methods for self-driving cars. At the end of this course, there is a project that implements the effectless Kalman filter and implements it to be used in a real self-driving car or autonomous vehicle.

What you will learn

  • How to use the linear Kalman filter to solve linear optimal estimation problems
  • How to use the extended Kalman filter to solve nonlinear estimation problems
  • How to use the ineffective Kalman filter to solve nonlinear estimation problems
  • How to combine measurements from multiple sensors all running at different update rates
  • How to tune the Kalman filter for best performance
  • How to set up a Kalman filter properly
  • How to model sensor errors in the Kalman filter
  • How to use error detection to eliminate bad sensor measurements
  • How to implement the above 3 Kalman filter variants in C++
  • How to implement LKF in C++ for a 2D tracking problem
  • How to implement EKF and UKF in C++ for autonomous car problem

Who is this course suitable for?

  • University or independent students
  • Robotics engineers or self-driving cars
  • Working engineers and scientists
  • Engineering professionals who want to review mathematical theory and skills related to Kalman filtering and sensor fusion
  • Software developers who want to understand the basic concepts behind data integration to help implement or support data integration code development.
  • Anyone who is good at mathematical theory and wants to learn how to implement theory in code.

Advanced Kalman Filtering and Sensor Fusion course specifications

  • Publisher: Udemy
  • teacher : Steven Dumbledore
  • English language
  • Education level: Intermediate
  • Number of courses: 82
  • Training duration: 8 hours and 20 minutes

Chapters of the Advanced Kalman Filtering and Sensor Fusion course

Course prerequisites

  • A curious mind!
  • Basic Calculus: Functions, Derivatives, Integrals
  • Linear Algebra: Matrix and Vector Operations
  • Basic Probability
  • Basic C++ Programming Knowledge


Advanced Kalman Filtering and Sensor Fusion

Sample video

Installation guide

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English subtitle

Quality: 720p

download link

Download part 1 – 1 GB

Download part 2 – 1 GB

Download part 3 – 161 MB

File(s) password:


2.15 GB

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