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The major themes of this course are estimation and control of dynamic systems. Preliminary topics begin with reviews of probability and random variables. Next, classical and state-space descriptions of random processes and their propagation through linear systems are introduced, followed by frequency domain design of filters and compensators. And Kalman filter is employed to estimate the states of dynamic systems will be discussed.  


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: MIT

Course Curriculum

Module 01
Introduction 00:20:00
Independence 00:20:00
Expectation, Averages and Characteristic Function 00:15:00
Correlation, Covariance, and Orthogonality 00:15:00
Some Common Distributions 00:15:00
More Common Distributions 00:10:00
Linearized Error Propagation 00:15:00
More Linearized Error Propagation 00:10:00
Concept of a Random Process 00:15:00
Autocorrelation Function 00:10:00
Power Spectral Density Function 00:15:00
Gauss-Markov Process 00:15:00
Module 02
Determination of Autocorrelation 00:15:00
Introduction: The Analysis Problem 00:15:00
Pure White Noise and Bandlimited Systems 00:10:00
Nonstationary (Transient) Analysis – Initial Condition Response 00:15:00
The Wiener Filter Problem 00:10:00
Orthogonality 00:10:00
Complementary Filter 00:10:00
Estimation 00:10:00
Markov Processes 00:10:00
State Space Description 00:15:00
Monte Carlo Simulation of Discrete-Time Systems 00:10:00
Transition from the Discrete to Continuous Filter Equations 00:10:00
Divergence Problems 00:15:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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