outline - (a) least mean square (LMS) algorithm and...

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University of New Mexico, Albuquerque Department of Electrical and Computer Engineering ECE-595, Section 011: Adaptive Filtering Fall Semester 2011, 3 Credit Hours COURSE INFORMATION: Course Instructor Prof. Balu Santhanam Office Location Room 326A, EECE Bldg. Contact Info Email: bsanthan@eece.unm.edu Tel: (505) 277-1611 , Fax: (505) 277-1439 Prerequisite ECE-541, ECE-539, knowledge of MATLAB Location ECE-210 Lectures TR: 2:00 - 3:15 PM Textbook Simon Haykin, ”Adaptive Filter Theory” Fourth edition, Prentice Hall Inc., New York, 2002. Office Hours TBA GRADING SCHEME: Problem Sets/Computer Projects : 40% Midterm : 30% Final Exam : Dec 13th, Tue, 10:00 - 12:00: 30% COURSE OUTLINE: The material covered in this course deals with aspects of adaptive filter theory and issues relating to the design of adaptive signal processing algorithms. This course has two aspects: (a) theoretical component, (b) applications component. 1. Theoretical : We will specifically look at some of the popular adaptive filtering algorithms:
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Unformatted text preview: (a) least mean square (LMS) algorithm and variants, (b) recursive least squares (RLS) algo-rithm and variants, (c) constant modulus algorithm (CMA) algorithm, (d) adaptive correla-tion enhancement (ACE) algorithm, (e) Kalman ltering, (f) extended Kalman ltering and variants. We will look at both the theoretical foundations as well as convergence issues in each case. 2. Applications : For this aspect of the course we will look at the applications of adaptive ltering techniques to the problems of : (a) adaptive noise cancelation, (b) multiple target tracking, (c) channel equalization, (d) interference cancelation, (e) biomedical signal pro-cessing applications. The applications component will involve several MATLAB simulation exercises and literature survey in specic areas of adaptive ltering. 1...
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