Course Information

Course Name	Turkish	Spec.Top.in Aeron.&Astron.Eng.
	English	Spec.Top.in Aeron.&Astron.Eng.
Course Code	UUM 543E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	-	3	3	-	-
Course Language		English
Course Coordinator		İsmail Bayezit Oktay Arslan
Course Objectives		The course aims to provide practical knowledge and skills for engineers and scientists wishing to employ and develop advanced estimation methods. The focus of the course is on the (linear) Kalman filter and the (non-linear) extended Kalman filter and their use in sensor-based estimation problems. By the end of the course, students should know how to use estimation and filtering algorithms to solve a variety of navigation problems in aerospace applications.
Course Description		Applied Kalman Filtering
Course Outcomes		Topics: 1. Probability, random variables, random processes 2. Least squares estimation 3. Discrete Kalman filter 4. Continuous Kalman filter 5. Extended Kalman filter and additional topics 6. Unscented Kalman and particle filters
Pre-requisite(s)		Probability, Linear Algebra, Matlab/Simulink and Phyton
Required Facilities		Zoom Lectures
Other		Policies: Homework is due at the beginning of class on the day it is due. Late homework will normally be graded, but will not necessarily receive any points. Students are encouraged to discuss homework verbally with each other, but you may not work together when preparing written answers – nor may written answers be compared. Violation of this policy is a violation of the Istanbul Technical University Academic Honor Code, and will be dealt with accordingly. For any questions involving these or any other Academic Honor Code issues, please consult me or https://www.sis.itu.edu.tr/TR/mevzuat/akademik-onur-sozu-esaslar.php
Textbook		D. Simon, Optimal State Estimation, Wiley, 2006. (class text)
Other References		Selected References: 1. D. Simon, Optimal State Estimation, Wiley, 2006. (class text) 2. A. Gelb, Applied Optimal State Estimation, MIT Press, 1974. 3. S. Sarkka, Bayesian Filtering and Smoothing, Cambridge University Press, 2013 4. R. G. Brown and P. Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, 4th Edition, Wiley, 2012. 5. G. J. Bierman, Factorization Method for Discrete Sequential Estimation, Dover, 1977. 6. Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation, Wiley, 2001 7. P. Zarchan and H. Musoff, Fundamentals of Kalman Filtering: A Practical Approach, 3rd Edition, AIAA, 2009 8. R. Stengel, Optimal Control and State Estimation, Dover, 1986/1994

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