courses:kalman_course
Table of Contents
DASL 108: Kalman Filtering
Author: Blake Hament Email: blakehament@gmail.com
Date: Last modified on 10/30/2018
Overview
This course is an introduction to Kalman Filtering. It begins with review of important background topics, then develops and explores the algorithms for linear and nonlinear Kalman Filtering. The last part of the course requires students to collect data with a noisy IMU and write their own Kalman Filters to estimate the ground truth.
The class is organized roughly as follows:
- Background (4 hours): Statistics, Optimization, Linear Algebra, Bayesian Probability
- Kalman Filtering and Extended Kalman Filtering (4 hours)
- Practical Application (4 hours)
Prerequisites
- Basic Dynamics [ME 230]
- Some Statistics and Linear Algebra
- Matlab or other coding language
Lecture Notes
Topic Outline: Kalman Short Course Outline
Excellent Beginner tutorial: https://home.wlu.edu/~levys/kalman_tutorial/
Probabilistic Robotics: http://www.probabilistic-robotics.org/
Background PPTs
Main PPTs
Homework
Final Exam
courses/kalman_course.txt · Last modified: 2019/01/08 15:38 by blakehament