====== DASL 108: Kalman Filtering ======

**Author:** [[:unlv_hament|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: {{:courses:kalman_course_outline.pdf|Kalman Short Course Outline}}

Excellent Beginner tutorial: https://home.wlu.edu/~levys/kalman_tutorial/

Probabilistic Robotics: http://www.probabilistic-robotics.org/

=== Background PPTs ===

[[https://drive.google.com/open?id=1I8ZOl2fAzHjEHcqDgXr89ktyM08MeYx8|Types of Noise]]

[[https://drive.google.com/open?id=10TZ068BH7z36b0x_1OsJhFhi-nzOJRsz|Least Squares Regression]]

[[https://drive.google.com/open?id=1L17RXQa93y2Ho3CLOT7PeMIwxpAvslVS|Variance and STD]]

[[https://drive.google.com/open?id=1GKTGGg2BCFT-Gfuc3OlsoCnqAaau3_Iw|Riccatti Equation]]

=== Main PPTs ===

[[https://drive.google.com/open?id=1k6e-1LZRZuXLMtr-rweNS3j9WO2JBq_M|Bayesian Probability and Filtering]]

[[https://drive.google.com/open?id=1vyoIWADYM3wmsf9POu4cJAlW3zcH3PvA|Kalman Filtering]]

===== Homework =====

{{:courses:kalman_course_pre_assessment.pdf|Pre-Assessment}}

===== Final Exam=====

[[https://docs.google.com/document/d/1B9VCrYLD-kiPc8xqQIR8ni0j3N1fosdn2V5hZhti5SE/edit|Exam]]