Detection, classification, and estimation in the (t,f) domain

Sayeed, A. M., Papandreou-Suppappola, A., Suppappola, S. B., Xia, X. -G., Hlawatsch, F., Matz, G., Boashash, B., Azemi, G. and Khan, N. A. (2016). Detection, classification, and estimation in the (t,f) domain. In Boualem Boashash (Ed.), Time-frequency signal analysis and processing: a comprehensive reference Second edition ed. (pp. 693-743) Amsterdam, Netherlands: Academic Press. doi:10.1016/B978-0-12-398499-9.00012-1

Author Sayeed, A. M.
Papandreou-Suppappola, A.
Suppappola, S. B.
Xia, X. -G.
Hlawatsch, F.
Matz, G.
Boashash, B.
Azemi, G.
Khan, N. A.
Title of chapter Detection, classification, and estimation in the (t,f) domain
Formatted title
Detection, classification, and estimation in the (t,f) domain
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/B978-0-12-398499-9.00012-1
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 12
Start page 693
End page 743
Total pages 51
Total chapters 18
Collection year 2017
Language eng
Formatted Abstract/Summary
Several studies involving real-life applications have shown that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency ((t, f)) characteristics of such signals. Such (t, f) formulations are described in this chapter and include (t, f) matched filtering for detection and extraction of (t, f) features for classification. The topic is covered in six sections with appropriate internal cross-referencing to this and other chapters. The structure of (t, f) methods is suitable for designing and implementing optimal detectors. Several approaches exist, such as decomposition of TFDs into sets of spectrograms (Section 12.1). For both analysis and classification, a successful (t, f) methodology requires matching of TFDs with the structure of the signal. This can be achieved by a matching pursuit algorithm using (t, f) atoms adapted to the analyzed signals (Section 12.2). We can perform system identification by exciting linear systems with a linear FM signal and relating TFDs of the input and output using (t, f) filtering techniques (Section 12.3). Methods for (t, f) signal estimation and detection can be carried out using time-varying Wiener filters (Section 12.4). The last two sections present advanced formulations and methods for (t, f) matched filtering (Section 12.5) and the formulation of (t, f) features for classification (Section 12.6), both of which are applied to a serious medical problem as an illustration of the performance gained.
Q-Index Code B1
Q-Index Status Provisional Code
Institutional Status UQ

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