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Erschienen in:
Introduction to Compressed Sensing and Sparse Filtering Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

7. Sparse Nonlinear MIMO Filtering and Identification

verfasst von : G. Mileounis, N. Kalouptsidis

Erschienen in: Compressed Sensing & Sparse Filtering

Verlag: Springer Berlin Heidelberg

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Abstract

In this chapter system identification algorithms for sparse nonlinear multi input multi output (MIMO) systems are developed. These algorithms are potentially useful in a variety of application areas including digital transmission systems incorporating power amplifier(s) along with multiple antennas, cognitive processing, adaptive control of nonlinear multivariable systems, and multivariable biological systems. Sparsity is a key constraint imposed on the model. The presence of sparsity is often dictated by physical considerations as in wireless fading channel–estimation. In other cases it appears as a pragmatic modelling approach that seeks to cope with the curse of dimensionality, particularly acute in nonlinear systems like Volterra type series. Three identification approaches are discussed: conventional identification based on both input and output samples, Semi-Blind identification placing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulations.

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Vorheriges Kapitel Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks
Nächstes Kapitel Optimization Viewpoint on Kalman Smoothing with Applications to Robust and Sparse Estimation
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Metadaten
Titel
Sparse Nonlinear MIMO Filtering and Identification
verfasst von
G. Mileounis
N. Kalouptsidis
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
Buch
Compressed Sensing & Sparse Filtering

Print ISBN: 978-3-642-38397-7

Electronic ISBN: 978-3-642-38398-4

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-642-38398-4_7

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