Multisensor, multichannel, and time-space processing

Boashash, B., Ali, S., Amin, M. G., Zhang, Y. D., Abed-Meraim, K., Belouchrani, A., Leyman, A. R., Linh-Trung, N., Abed-Meraim, K. and Aissa-El-Bey, A. (2016). Multisensor, multichannel, and time-space processing. In Boualem Boashash (Ed.), Time-frequency signal analysis and processing: a comprehensive reference Second edition ed. (pp. 453-518) Amsterdam, Netherlands: Academic Press. doi:10.1016/B978-0-12-398499-9.00008-X

Author Boashash, B.
Ali, S.
Amin, M. G.
Zhang, Y. D.
Abed-Meraim, K.
Belouchrani, A.
Leyman, A. R.
Linh-Trung, N.
Abed-Meraim, K.
Aissa-El-Bey, A.
Title of chapter Multisensor, multichannel, and time-space processing
Title of book Time-frequency signal analysis and processing: a comprehensive reference
Place of Publication Amsterdam, Netherlands
Publisher Academic Press
Publication Year 2016
Sub-type Research book chapter (original research)
DOI 10.1016/B978-0-12-398499-9.00008-X
Open Access Status Not yet assessed
Series EURASIP and Academic Press series in signal and image processing
Edition Second edition
ISBN 9780123984999
Editor Boualem Boashash
Chapter number 8
Start page 453
End page 518
Total pages 66
Total chapters 18
Language eng
Formatted Abstract/Summary
This chapter presents time-frequency (t, f) methods suitable for multichannel signal processing using multisensor and time-space processing methods. The topic is covered in seven sections with relevant cross-referencing. A brief tutorial review of the topic of multichannel/multisensor (t, f) signal processing describes the extension of (t, f) methods to incorporate the spatial diversity information provided by multisensor recordings; this is illustrated on an application to brain electroencephalogram (EEG) abnormality source localization (Section 8.1). The multichannel data can then be processed with time-frequency distributions (TFDs) for channel estimation and equalization. In blind source separation (BSS) and direction of arrival (DOA) estimation problems, the (t, f) approach to array signal processing leads to improved spatial resolution and source separation performances. Methods include (t, f) multiple signal classification (MUSIC), and TFD-based BSS (Section 8.2). In sensor array processing, for source localization, TFDs provide a good framework for hypothesis testing, and they allow the
optimal detector to be implemented naturally and efficiently (Section 8.3). In underwater acoustics and telecommunications, separation of signal mixtures is traditionally based on methods such as independent component analysis (ICA) or BSS. These can be formulated using TFDs for dealing with the case when the signals are nonstationary (Section 8.4). In the underdetermined case, the (t, f) formulations, methodologies, and algorithms for BSS are implemented using vector clustering and component extraction (Section 8.5). Then, Section 8.6 describes a method where audio source localization and separation can be improved using multisensor (t, f) analysis. Finally, in Section 8.7, a selection of basic algorithm and MATLAB code is provided so as to allow the reader to easily reproduce the results in this chapter.
Q-Index Code B1
Q-Index Status Provisional Code
Institutional Status UQ

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