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