Robust Distributed Diffusion Recursive Least Squares Algorithms With Side Information for Adaptive Networks

This work develops robust diffusion recursive least-squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent c...

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Vydáno v:IEEE transactions on signal processing Ročník 67; číslo 6; s. 1566 - 1581
Hlavní autoři: Yu, Yi, Zhao, Haiquan, de Lamare, Rodrigo C., Zakharov, Yuriy, Lu, Lu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.03.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptive algorithms
Algorithms
Atmospheric measurements
Complexity theory
Computer simulation
Convergence
dichotomous coordinate-descent
diffusion cooperation
Distributed algorithms
Estimation
impulsive noises
Least squares
Performance degradation
recursive least squares algorithms
Regression analysis
Robustness
Signal processing algorithms
Symmetric matrices
Time dependence
ISSN:1053-587X, 1941-0476
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Shrnutí:This work develops robust diffusion recursive least-squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however, its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2019.2893846