QRD-RLS Adaptive Filtering

APOLINARIO JR, JOSE

ISBN 10: 0387097333 / ISBN 13: 9780387097336

Editore: Springer, 2009

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Dati bibliografici

• Titolo: QRD-RLS Adaptive Filtering
• Editore: Springer
• Data di pubblicazione: 2009
• Lingua: Inglese
• ISBN 10: 0387097333
• ISBN 13: 9780387097336
• Rilegatura: Rilegato
• Condizione: New

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Riassunto

I feel very honoured to have been asked to write a brief foreword for this book on QRD-RLS Adaptive Filtering–asubjectwhichhas been close to my heart for many years. The book is well written and very timely – I look forward personally to seeing it in print. The editor is to be congratulated on assembling such a highly esteemed team of contributing authors able to span the broad range of topics and concepts which underpin this subject. In many respects, and for reasons well expounded by the authors, the LMS al- rithm has reigned supreme since its inception, as the algorithm of choice for prac- cal applications of adaptive ltering. However, as a result of the relentless advances in electronic technology, the demand for stable and ef cient RLS algorithms is growing rapidly – not just because the higher computational load is no longer such a serious barrier, but also because the technological pull has grown much stronger in the modern commercial world of 3G mobile communications, cognitive radio, high speed imagery, and so on.

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QRD-RLS Adaptive Filtering covers some of the most recent developments as well as the basic concepts for a complete understanding of the QRD-RLS-based adaptive filtering algorithms. It presents this research with a clear historical perspective which highlights the underpinning theory and common motivating factors that have shaped the subject.

The material is divided into twelve chapters, going from fundamentals to more advanced aspects. Different algorithms are derived and presented, including basic, fast, lattice, multichannel and constrained versions. Important issues, such as numerical stability, performance in finite precision environments and VLSI oriented implementations are also addressed. All algorithms are derived using Givens rotations, although one chapter deals with implementations using Householder reflections.

QRD-RLS Adaptive Filtering is a useful reference for engineers and academics in the field of adaptive filtering.