BEE 512
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BEE 512 (statistical) Signal Processing II
Abridged Course Syllabus
B EE 512 provides an introduction to statistical signal processing which deals with random signals, their modeling, characterization and transformation to extract information about the underlying processes or mechanisms that generate them. The course covers representation of signals using parametric modeling, parametric and non-parametric methods of spectrum estimation, optimum filtering in time and frequency domains, least-square estimation and prediction and adaptive filtering and their applications. MATLAB is used in the course to simulate signals and implement algorithms.
Major topics of the course are the following:
- Introduction to statistical signal processing and its applications.
- Review of random signals and stochastic processes.
- Signal modelling.
- Least-squares filtering and prediction.
- Power spectrum estimation.
- Optimum linear filtering.
- Kalman Filtering
- Adaptive filtering.
Course Learning Goals:
After successfully completing this course, you will be able to
- Describe random signals using parametric models.
- Estimate power spectrum of signals using parametric and non-parametric methods.
- Design and implement optimum filters and Kalman filters.
- Perform least-square filtering and prediction.
- Design and analyze adaptive filters.
- Use MATLAB for implementing algorithms and processing signals.
Textbook:
- M. H. Hayes, “Statistical Digital Signal Processing and Modelling,” 1996, John Wiley & Sons Inc., ISBN-0-471-59431-8.
Grading Criteria: Your grade for the course will depend upon the following elements:
Component |
Percentage of Final Grade |
Homework |
20% |
Project |
20% |
Midterm examination |
30% |
Final examination |
30% |
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