Omfattning: 5

Tidtabel: 12.09.2017 - 24.10.2017

Undervisningsperiod (är i kraft 01.08.2018-31.07.2020): 

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

Lärandemål (är i kraft 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.

Innehåll (är i kraft 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.

Metoder, arbetssätt och bedömningsgrunder (är i kraft 01.08.2018-31.07.2020): 

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

Arbetsmängd (är i kraft 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

Studiematerial (är i kraft 01.08.2018-31.07.2020): 

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

Ersättande prestationer (är i kraft 01.08.2018-31.07.2020): 

Replaces the course AS-84.3128.

Kursens webbplats (är i kraft 01.08.2018-31.07.2020): 

https://mycourses.aalto.fi/course/search.php?search=ELEC-E8104

Förkunskaper (är i kraft 01.08.2018-31.07.2020): 

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

Bedömningsskala (är i kraft 01.08.2018-31.07.2020): 

0-5

Tilläggsinformation (är i kraft 01.08.2018-31.07.2020): 

language class 3: English

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