Credits: 5

Schedule: 10.09.2019 - 22.10.2019

Teaching Period (valid 01.08.2018-31.07.2020): 

I (autumn) 2018 - 2019
I (autumn) 2019 - 2020

Learning Outcomes (valid 01.08.2018-31.07.2020): 

The student understands the main concepts in stochastics, mathematics and main concepts related to estimation and state estimation, the role of uncertainty in dynamic systems and is able to implement state filtering algorithms both in linear and nonlinear case. The student also can use the tools of stochastic systems identification.

Content (valid 01.08.2018-31.07.2020): 

Basics of statistics and stochastic processes. Basic concepts in estimation, ML, MAP, LS, MMSE; unbiased estimators. Linear estimation in static systems. Optimal state estimation in discrete linear dynamic systems, Kalman filter and information filter. Optimal State estimation in nonlinear dynamic systems, recursive functional relationship. Approximation of optimal nonlinear state estimation, particle filter, extended Kalman filters, 1st and 2nd order. Adaptive estimation. Stochastic system identification.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Final exam (80%), assignments (20%).

Workload (valid 01.08.2018-31.07.2020): 

Contact teaching, independent studies and work based-learning, examination

Contact teaching: 6 X 4 +4 x 2 = 32 h
Indepenedent work: 103 h

Study Material (valid 01.08.2018-31.07.2020): 

Yaakov Bar-Shalom, et al: Estimation with applications to tracking and navigation (2001), handouts.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

Replaces the course AS-84.3128.

Course Homepage (valid 01.08.2018-31.07.2020):

Prerequisites (valid 01.08.2018-31.07.2020): 

Basic knowledge of control engineering and robotics, basic probability theory and statistics.

Grading Scale (valid 01.08.2018-31.07.2020): 


Further Information (valid 01.08.2018-31.07.2020): 

language class 3: English


Registration and further information