Adaptative Filtering and Linear Algebra DSP

Overview

Recent advances in the computational capabilities of DSP hardware have allowed complex DSP techniques such as equalisation, smart antennas, noise cancellation and MIMO systems to be implemented cost effectively. Adaptive signal processing lies at the core of these DSP techniques.

Course Aim

The aim of this course is to educate participants in the theory and applications of digital adaptive filtering algorithms and architectures. The course considers the use of established linear algebra techniques for applications in audio, wireless and mobile communications such as fast equalisation, noise cancellation, beamforming and MIMO systems. A comprehensive description of adaptive filtering algorithms, architectures and applications is provided. This is complemented with case studies in the areas of audio and digital communication.

The course will include:

• Linear Algebra Review

• Adaptive DSP Applications

• Matrix Inversion Methods

• Adaptive Filter Implementation Issues

• Adaptive Filtering Architectures

• Audio and Digital Communication Case Studies

• LMS, RLS, APA and QR Algorithms.

Course Presentation

The course format is:

• 60% Lectures

• 30% Hands-on Labs (simulation based)

• 10% Demonstrations.

Laboratory Sessions

Professional DSP design software will be used for the laboratory sessions. This advanced software provides a comprehensive and state of the art DSP toolbox for modern signal processing. Steepest Ascent's adaptive filtering and equalisation simulation libraries will also be used.