Adaptive Distributed Estimation Based on Recursive Least-Squares and Partial Diffusion

Using the diffusion strategies, an unknown parameter vector can be estimated over an adaptive network by combining the intermediate estimates of neighboring nodes at each node. We propose an extension to the diffusion recursive least-squares algorithm by allowing partial sharing of the entries of th...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 62; no. 14; pp. 3510 - 3522
Main Authors: Arablouei, Reza, Dogancay, Kutluyil, Werner, Stefan, Huang, Yih-Fang
Format: Journal Article
Language:English
Published: New York, NY IEEE 15.07.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive networks
Adaptive systems
Algorithm design and analysis
Algorithms
Applied sciences
Convergence
Detection, estimation, filtering, equalization, prediction
Diffusion
diffusion adaptation
distributed estimation
Educational institutions
Electronic mail
Estimates
Estimation
Exact sciences and technology
Information, signal and communications theory
Least squares method
Mathematical analysis
partial diffusion
Recursive
recursive least-squares
Signal and communications theory
Signal processing algorithms
Signal, noise
Telecommunications and information theory
Vectors
Vectors (mathematics)
Performance evaluation
distributed estimation
Adaptive estimation
Recursive algorithm
Steady state
recursive least-squares
partial diffusion
Adaptive networks
Distributed processing
Simulation
Least squares method
Recursive method
Signal processing
Partial least squares
diffusion adaptation
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:Using the diffusion strategies, an unknown parameter vector can be estimated over an adaptive network by combining the intermediate estimates of neighboring nodes at each node. We propose an extension to the diffusion recursive least-squares algorithm by allowing partial sharing of the entries of the intermediate estimate vectors among the neighbors. Accordingly, the proposed algorithm, termed partial-diffusion recursive least-squares (PDRLS), enables a trade-off between estimation performance and communication cost. We analyze the performance of the PDRLS algorithm and prove its convergence in both mean and mean-square senses. We also derive a theoretical expression for its steady-state mean-square deviation. Simulation results substantiate the efficacy of the PDRLS algorithm and demonstrate a good match between theory and experiment.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2014.2327005