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Published by: Massachusetts Institute of Technology | Language: English
Published by: Massachusetts Institute of Technology | Language: English
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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 compensator
Author(s):
Tag(s):
- aeronautics and astronautics
- probability
- stochastic estimation
- estimation
- random variables
- random processes
- state space
- wiener filter
- control system design
- kalman filter
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Published by: Massachusetts Institute of Technology | Language: English
Published by: Massachusetts Institute of Technology | Language: English
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This course examines the fundamentals of detection and estimation for signal processing, communications, and control. Topics covered include: vector spaces of random variables; Bayesian and Neyman-Pearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimum-variance unbiased estimators and the Cramer-Rao bounds; represen
Author(s):
Tag(s):
- electrical engineering and computer science
- stochastic process
- detection
- estimation
- signal processing
- communications
- control
- vector spaces
- bayesian
- neyman-pearson
- minimum-variance unbiased estimator
- cramer-rao bounds
- shaping filter
- whitening filter
- karhunen-loeve expansion
- waveform observation
- linear prediction
- spectral estimation
- wiener filter
- kalman filter
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