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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
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English [CC]
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Description
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. From there, the Kalman filter is employed to estimate the states of dynamic systems. Concluding topics include conditions for stability of the filter equations.
Course content
- Introduction Random Signals Unlimited
- Independence Unlimited
- Expectation, Averages and Characteristic Function Unlimited
- Correlation, Covariance, and Orthogonality Unlimited
- Some Common Distributions Unlimited
- More Common Distributions Unlimited
- Linearized Error Propagation Unlimited
- More Linearized Error Propagation Unlimited
- Concept of a Random Process Unlimited
- Autocorrelation Function Unlimited
- Power Spectral Density Function Unlimited
- Gauss-Markov Process Unlimited
- Determination of Autocorrelation and Spectral Unlimited
- Introduction: The Analysis Problem Unlimited
- Pure White Noise and Bandlimited Systems Unlimited
- Nonstationary (Transient) Analysis – Initial Condition Response Unlimited
- The Wiener Filter Problem Unlimited
- The Stationary Optimization Problem – Weighting Function Approach Unlimited
- Complementary Filter Perspective Unlimited
- Estimation Unlimited
- Markov Processes Unlimited
- State Space Description Unlimited
- Monte Carlo Simulation of Discrete-Time Systems Unlimited
- Transition from the Discrete to Continuous Unlimited
- Divergence Problems Unlimited
Instructor
Massachusetts Institute of Technology
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