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 ﬁltering, (f) extended Kalman ﬁltering 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 ﬁltering 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 speciﬁc areas of adaptive ﬁltering. 1...
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This note was uploaded on 12/02/2011 for the course AR 107 taught by Professor Gracegraham during the Fall '11 term at Montgomery College.
- Fall '11