A Proportionate Diffusion LMS Algorithm for Sparse Distributed Estimation

We propose a new diffusion least mean squares algorithm that utilizes adaptive gains in the adaptation stage for the sparse distributed estimation problem. We derive the optimal gains that attain a minimum mean-square deviation and propose an adaptive gain control method. We provide the mean stabili...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2015-10, Vol.62 (10), p.992-996
Main Authors: Yim, Sung-Hyuk, Lee, Han-Sol, Song, Woo-Jin
Format: Article
Language:English
Subjects:
Adaptive control systems
Adaptive network
Algorithm design and analysis
Algorithms
Circuits and systems
Convergence
Deviation
Diffusion
distributed estimation
Estimation
Gain
Least squares approximations
Mathematical analysis
Noise
Optimization
proportionate-type LMS
sparsity
Stability analysis
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Summary:We propose a new diffusion least mean squares algorithm that utilizes adaptive gains in the adaptation stage for the sparse distributed estimation problem. We derive the optimal gains that attain a minimum mean-square deviation and propose an adaptive gain control method. We provide the mean stability analysis to establish sufficient condition for the algorithm to converge in the mean sense. The algorithm achieves higher convergence speed than the sparsity-constrained algorithms, regardless of the sparsity of the vector of interest.
ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2015.2435631