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ThinkMind // ICWMC 2017, The Thirteenth International Conference on Wireless and Mobile Communications // View article icwmc_2017_6_20_20067


A Robust Distributed Notch Filtering Algorithm for Frequency Estimation Over Sensor Networks

Authors:
Wael Bazzi
Amir Rastegarnia
Azam Khalili
Mahtab Bahrami

Keywords: Adaptive networks; frequency estimation; diffusion; notch filter

Abstract:
In this paper, we consider the distributed frequency estimation problem where nodes of a network collaborate with each other to estimate the frequency of a single-frequency signal from measurements corrupted by impulsive noise. In the proposed algorithm, we reduce the impulsive noise effect by using the maximum correntropy criteria (MCC). The MCC is a robust optimality criterion for non-Gaussian signal processing. In the proposed algorithm, each node employs an adaptive notch filter to filter the input noisy measurements. The nodes collaborate with each other to optimize a cost function (given in terms of the MCC) in such a way that the filter output resembles as closely as possible, the desired signal. To derive the algorithm, we first formulate the distributed frequency estimation problem in terms of the MCC. Next, we use the iterative gradient ascent approach in our solution. The developed approach will be referred to as the diffusion notch filter-MCC (dNF-MCC) algorithm. The effectiveness of the proposed algorithm is demonstrated by computer simulations.

Pages: 95 to 99

Copyright: Copyright (c) IARIA, 2017

Publication date: July 23, 2017

Published in: conference

ISSN: 2308-4219

ISBN: 978-1-61208-572-2

Location: Nice, France

Dates: from July 23, 2017 to July 27, 2017

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