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A Spatial Diffusion Strategy for Tap-Length Estimation Over Adaptive Networks

Lookup NU author(s): Professor Jonathon Chambers

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Abstract

We consider the distributed estimation problem, where a set of nodes is required to collectively estimate some parameter vector of interest with unknown or variable tap-length. In practice, a sufficiently large filter length is utilized in such contexts to avoid a large excess mean square error at steady state, thereby resulting in slower convergence rate and increased computations. In this work we motivate and propose a new diffusion-based variable tap-length algorithm, which is able to track tap-length changes during the convergence process. Theoretical analyses are provided in terms of steady-state performance and convergence performance, which are verified by simulation results. Some general criteria for parameter selections are also given according to the performance analyses. Numerical simulations demonstrate the efficiency of the proposed algorithm as compared with existing techniques, and robustness to parameter settings provided the parameter choice guidelines are satisfied.

Publication metadata

Author(s): Zhang YG, Wang CC, Zhao L, Chambers JA

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Signal Processing

Year: 2015

Volume: 63

Issue: 17

Pages: 4487-4501

Print publication date: 01/09/2015

Online publication date: 02/06/2015

Acceptance date: 15/05/2015

Date deposited: 08/02/2017

ISSN (print): 1053-587X

ISSN (electronic): 1941-0476

Publisher: IEEE

URL: http://dx.doi.org/10.1109/TSP.2015.2440182

DOI: 10.1109/TSP.2015.2440182

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