In this section
@article{Neo:2023:10.1109/MSP.2023.3269200,
author = {Neo, VW and Redif, S and McWhirter, JG and Pestana, J and Proudler, IK and Weiss, S and Naylor, PA},
doi = {10.1109/MSP.2023.3269200},
journal = {IEEE: Signal Processing Magazine},
pages = {18--37},
title = {Polynomial eigenvalue decomposition for multichannel broadband signal processing},
url = {http://dx.doi.org/10.1109/MSP.2023.3269200},
volume = {40},
year = {2023}
}
TY - JOUR
AB - This article is devoted to the polynomial eigenvalue decomposition (PEVD) and its applications in broadband multichannel signal processing, motivated by the optimum solutions provided by the eigenvalue decomposition (EVD) for the narrow-band case [1], [2]. In general, the successful techniques from narrowband problems can also be applied to broadband ones, leading to improved solutions. Multichannel broadband signals arise at the core of many essential commercial applications such as telecommunications, speech processing, healthcare monitoring, astronomy and seismic surveillance, and military technologies like radar, sonar and communications [3]. The success of these applications often depends on the performance of signal processing tasks, including data compression [4], source localization [5], channel coding [6], signal enhancement [7], beamforming [8], and source separation [9]. In most cases and for narrowband signals, performing an EVD is the key to the signal processing algorithm. Therefore, this paper aims to introduce PEVD as a novel mathematical technique suitable for many broadband signal processing applications.
AU - Neo,VW
AU - Redif,S
AU - McWhirter,JG
AU - Pestana,J
AU - Proudler,IK
AU - Weiss,S
AU - Naylor,PA
DO - 10.1109/MSP.2023.3269200
EP - 37
PY - 2023///
SN - 1053-5888
SP - 18
TI - Polynomial eigenvalue decomposition for multichannel broadband signal processing
T2 - IEEE: Signal Processing Magazine
UR - http://dx.doi.org/10.1109/MSP.2023.3269200
UR - http://hdl.handle.net/10044/1/106621
VL - 40
ER -
For more information about the group, please contact:
Dr Dan Goodman
+44 (0)20 7594 6264
d.goodman@imperial.ac.uk