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Adaptive algorithms for sparse system identification
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DOI：10.1016/j.sigpro.2011.02.013.png)
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Abstract
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In this paper, identification of sparse linear and nonlinear systems is considered via compressive sensing methods. Efficient algorithms are developed based on Kalman filtering and Expectation-Maximization. The proposed algorithms are applied to linear and nonlinear channels which are represented by sparse Volterra models and incorporate the effect of power amplifiers. Simulation studies confirm significant performance gains in comparison to conventional non-sparse methods. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Adaptive estimation
Compressive sensing
Kalman filtering
Expectation-Maximization
Volterra series
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