Fundamentals Of Adaptive Filtering

This book is intended for a graduate course on adaptive filtering and is based on the author's course offered at ucla over a number of years. Each chapter in the book consists of five distinctive parts in the following order: concepts, notes and bibliography, problems, appendixes and computer projects. The computer projects have been chosen to be relevant for practitioners and to show students how the theory can be applied to real situations. Optimal estimation. Linear estimation. Constrained linear estimation. Steepest-descent algorithms. Stochastic-gradient algorithms. Steady-state performance of adaptive filters. Tracking performance of adaptive filters. Finite precision effects. Transient performance of adaptive filters. Block adaptive filters. The least-squares criterion. Recursive least-squares. Rls array algorithms. Fast fixed-order filters. Lattice filters. Laguerre adaptive filters. Robust adaptive filters.