Purpose:

To enable the students to apply theories and methods for design, analysis and realisation of

adaptive transversal and Lattice filters.

To give the students a general knowledge of adaptive IIR-filters, Kalman-filters incl.

methods for adaptive processing of multiple input-signals.

Contents:

Adaptive MA-filters

Performance functions, the Wiener solution and gradient search

Newtons method, and Steepest Descent.

LMS- and NLMS algorithms incl. Convergence and stability criteria.

Learning curves, step size, and fault adjustment of weight factors

RLS and fast RLS algorithms

Adaptive Lattice MA-filters

Adaptive ARMA-filter

Performance functions

Gradient based methods

Convergence analysis

Kalman-filters

State-space description of signal model

Innovation

State estimation

Adaptive signal processing based on multiple input

Signal and noise subspace

MUSIC algorithms

Prerequisites:

Linear Algebra (F7-1) and Signal Analysis and Detection (SB7-1)

Duration:

2 ECTS

Category:

Project theme course (PE- course)