Advances in Subspace-Based Techniques for Signal Processing and Communications
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Editorial Advances in Subspace-Based Techniques for Signal Processing and Communications Kostas Berberidis,1 Benoit Champagne,2 George V. Moustakides,3 H. Vincent Poor,4 and Peter Stoica5 1 Department
of Computer Engineering and Informatics, University of Patras, 26500 Patras, Greece of Electrical and Computer Engineering, McGill University, 845 Sherbrooke Street, W Montreal, QC, Canada H3A 2T5 3 Department of Computer and Communication Engineering, University of Thessaly, 38221 Volos, Greece 4 Department of Electrical Engineering, Princeton University, Olden Street, Princeton, NJ 08544, USA 5 Department of Information Technology, Uppsala University, 751 05 Uppsala, Sweden 2 Department
Received 21 June 2006; Accepted 21 June 2006 Copyright © 2007 Kostas Berberidis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Research in subspace-based techniques for signal processing was initiated more than three decades ago, and there has been considerable progress in the area. Thorough studies have shown that the estimation and detection tasks in many signal processing and communications applications can be significantly improved by using the subspace-based methodology. Over the past few years new potential applications have emerged, and subspace methods have been adopted in several diverse fields such as smart antennas, sensor arrays, multiuser detection, system identification, time delay estimation, blind channel estimation, image segmentation, speech enhancement, learning systems, magnetic resonance spectroscopy, and radar systems. Subspace-based methods not only provide new insight into many such problems, but they also offer a good tradeoff between achieved performance and computational complexity. In most cases they can be considered to be low-cost alternatives to computationally intensive maximum-likelihood approaches. The interest of the signal processing community in subspace-based schemes remains strong as it is evident from the numerous articles and reports published in this area each year as well as from the attention that attracted the current special issue. The original goal of this special issue was to present stateof-the-art subspace techniques for modern signal processing applications and to address theoretical and implementation issues concerning this useful methodology. Judging from the contents of the issue and the high-quality papers it comprises, we believe that the goal has been reached. The special issue gathers eleven papers and exhibits a balance between theoretical results and application-oriented developments.
Although it is difficult to draw a line, we can distinguish two clusters of papers in this issue. The first cluster consists of six articles that are concerned with theoretical problems encountered in the subspace approach, while the second comprises five papers whose developments are related to specific application proble
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