Fundamentals - Spectral Analysis: Parametric and Non-Parametric Digital Methods - page 21

parameters. This approach is summarized in Chapter 4. The relevant spectral
quantities can be expressed in terms of this set of parameters, which helps the
following strategy: we implement parameter estimators, which are used to establish
in a second step spectral estimators, thus as by-products of the initial parametric
estimation.
A very special case is that of signals made up of a finite set of sinusoidal
functions , which are deterministic or random, possibly polluted by a random
additive phenomenon. This very special framework received great attention from the
signal and image processing community, as it helps model a large set of practical
situations. We have devoted a large amount of space to it (see sections 4.3 and 6.2.3,
and Chapter 8).
The tools necessary for the understanding of developments of Parts 2 and 3 are
summarized in the following chapters of this first part.
1.4. Bibliography
[AHM 75] AHMED N., RAO K.R., Orthogonal Transform for Digital Signal Processing,
Springer Verlag, 1975.
[DUV 91] DUVAUT P., Traitement du signal , Hermès, Paris, 1991.
[GAR 89] GARDNER W., Introduction to Random Processes , McGraw-Hill, 1989.
[HAR 69] HARMUTH H.F., Transmission of Information by Orthogonal Functions , Springer
Verlag, 1969.
[HLA 05] HLAWATSCH R., AUGER R., OVARLEZ J.-P., Temps-fréquence: concepts et
outils, IC2 series, Hermès Science, Paris, 2005.
[LAC 97] LACOUME J.-L., AMBLARD P.-O., COMON P., Statistiques d'ordre supérieur
pour le traitement du signal, Masson, 1997.
[LAC 00] LACAZE B., Processus aléatoires pour les communications numériques, Hermès,
Paris, 2000.
[NIK 93] NIKIAS C., PETROPULU A., Higher-Order Spectra Analysis, Prentice Hall, 1993.
[OPP 75] OPPENHEIM A., SCHAFFER R., Digital Signal Processing, Prentice Hall, 1975.