Welcome Slides

Statistical Signal Processing

Classical Estimation Theory: Basic concepts; properties of estimators; maximum likelihood; least squares. The theory will be linked to a “breakdown” of the localization problem.

Handout

James Hopgood, University of Edinburgh

Expectation Propagation for Scalable Inverse Problems in Imaging 

Dan Yao, Heriot-Watt University

Decision theory: Risk, optimal decisions, likelihood ratio test, connections with MAP and maximum likelihood estimation, types of errors, and Neyman-Pearson lemma

Joao Mota, Heriot-Watt University

Tracking and Sensing

Multiple targets, clutter and data association: The issues introduced by ambiguous association, combinatorics; Absolute assignment schemes (nearest neighbour); Probabilistic assignment schemes 

Mengwei Sun, University of Edinburgh

Source Separation

Source separation and beamforming background - Ian Proudler, University of Strathclyde

Algorithmic Enhancements to Polynomial Matrix Factorisations - Fraser Coutts,University of Edinburgh, Stephan Weiss, University of Strathclyde

Introduction to Spatial Filtering / Array Processing - Stephan Weiss, University of Strathclyde

Polynomial Matrix Algebra with Applications - Stephan Weiss, University of Strathclyde