2. Random signal models
Signal models involving random quantities are often used. There are two main reasons for this:
the need to model relatively large classes of signals, grouped by certain generic properties (e.g. audiophonic signals, the speech signal, images, etc.);
the need to model various types of "noise" (e.g. measurement noise), which are generally difficult to control.
The mathematical framework adapted to this situation is that of random processes. We won't go into probabilistic details here (a more detailed discussion can be found in
The Ultimate Scientific and Technical Reference
This article is included in
Signal processing and its applications
This offer includes:
Knowledge Base
Updated and enriched with articles validated by our scientific committees
Services
A set of exclusive tools to complement the resources
Practical Path
Operational and didactic, to guarantee the acquisition of transversal skills
Doc & Quiz
Interactive articles with quizzes, for constructive reading
Random signal models
1st signal processing step: signal analysis and estimation
Bibliography
Software tools
Peter Söndergaard. Ltfat, the linear time-frequency analysis toolbox (matlab/octave, freeware), 2009
The Mathworks. Matlab, 2009
Multiple authors. Mathtools.net,...
Websites
Rice University DSP group. Compressed sensing resources, 2009
http://www.dsp.ece.rice.edu/cs
Thomas Ströhmer. A first guided tour on the irregular sampling problem, 2000
http://www.math.ucdavis.edu/~strohmer/research/sampling/irsampl.html
...
The Ultimate Scientific and Technical Reference
