Week 1: Basic principle of adaptive filtering and estimation; probability, random variables, conditional and joint probability densities, statistical independence, correlation and covariance.
Week 2: Complex random variables, random vectors, correlation and covariance matrices, properties of Hermitian matrices (e.g., correlation / covariance matrices), positive definite forms, multivariate Gaussian density
Week 3: Concepts of random processes, wide sense stationary (WSS) processes and their correlation structures, power spectral density, parametric modeling of WSS processes – AR, MA and ARMA processes.
Week 4: Optimal FIR filters, real and complex valued optimal filters, method of steepest descent
Week 5: Least mean square (LMS) algorithm; convergence of LMS algorithm; normalized LMS, affine projection
Week 6: Examples of adaptive filters : channel equalization, echo cancellation, interference cancellation, line enhancement, beamforming etc.
Week 7: Limitations of LMS algorithm, formulation of recursive least squares (RLS) based adaptive filters, Moore-Penrose pseudo inverse, matrix inversion lemma
Week 8: Development of the RLS transversal adaptive filter, properties, variants of the RLS family.
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