Menu: Course View Options
Displaying course information from Spring 2014.
||1100 - 1220
|| T R
||245 Everitt Lab
||Basic concepts of random processes; linear systems with random inputs; Markov processes; spectral analysis; Wiener and Kalman filtering; applications to systems engineering. Course Information: Prerequisite: One of ECE 313, MATH 461, STAT 400.
||Credit in ECE 313 or MATH 461 or STAT 400 or STAT 410
|Detailed Description and Outline
- Review of basic probability: probability spaces, random variables, distribution and density functions, expectation, characteristic functions, conditional probability, conditional expectation
- Sequences of random variables: convergence concepts, laws of large numbers, central limit theorem, large deviations
- Random vectors and estimation: random vectors, covariance characterization, jointly Gaussian random variables, orthogonality principle, minimum mean squared error estimation, Kalman filtering
- Basic concepts of random processes: definition and classification, stationarity and ergodicity, correlation functions, continuity, differentiation, and integration of random processes
- Representations of random processes: sampling theorem, Karhunen-Loeve expansion, envelope representationadn simulation of narrowband processes
Special processes: Markov processes, Martingales, Wiener process, Poisson processes, shot noise, thermal noise, random walk
- Random processes in linear systems and Wiener filtering: spectral analysis of random processes in linear systems, the orthogonality principle, non-casual and casual Wiener filtering
H. Stark and J.W. Woods, Probability, Random Processes and Estimation Theory for Engineers, Prentice-Hall, 1994.
Last updated: 2/13/2013