Research output: Contribution to journal › Article › Scientific › peer-review

**Distributed autoregressive moving average graph filters.** / Loukas, Andreas; Simonetto, Andrea; Leus, Geert.

Research output: Contribution to journal › Article › Scientific › peer-review

Loukas, A, Simonetto, A & Leus, G 2015, 'Distributed autoregressive moving average graph filters' *IEEE Signal Processing Letters*, vol. 22, no. 11, pp. 1931-1935. https://doi.org/10.1109/LSP.2015.2448655

Loukas, A., Simonetto, A., & Leus, G. (2015). Distributed autoregressive moving average graph filters. *IEEE Signal Processing Letters*, *22*(11), 1931-1935. https://doi.org/10.1109/LSP.2015.2448655

Loukas A, Simonetto A, Leus G. Distributed autoregressive moving average graph filters. IEEE Signal Processing Letters. 2015;22(11):1931-1935. https://doi.org/10.1109/LSP.2015.2448655

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title = "Distributed autoregressive moving average graph filters",

abstract = "We introduce the concept of autoregressive moving average (ARMA) filters on a graph and show how they can be implemented in a distributed fashion. Our graph filter design philosophy is independent of the particular graph, meaning that the filter coefficients are derived irrespective of the graph. In contrast to finite-impulse response (FIR) graph filters, ARMA graph filters are robust against changes in the signal and/or graph. In addition, when time-varying signals are considered, we prove that the proposed graph filters behave as ARMA filters in the graph domain and, depending on the implementation, as first or higher order ARMA filters in the time domain.",

keywords = "Distributed time-varying computations, graph filters, graph Fourier transform, signal processing on graphs",

author = "Andreas Loukas and Andrea Simonetto and Geert Leus",

year = "2015",

doi = "10.1109/LSP.2015.2448655",

language = "English",

volume = "22",

pages = "1931--1935",

journal = "IEEE Signal Processing Letters",

issn = "1070-9908",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "11",

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AU - Simonetto, Andrea

AU - Leus, Geert

PY - 2015

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AB - We introduce the concept of autoregressive moving average (ARMA) filters on a graph and show how they can be implemented in a distributed fashion. Our graph filter design philosophy is independent of the particular graph, meaning that the filter coefficients are derived irrespective of the graph. In contrast to finite-impulse response (FIR) graph filters, ARMA graph filters are robust against changes in the signal and/or graph. In addition, when time-varying signals are considered, we prove that the proposed graph filters behave as ARMA filters in the graph domain and, depending on the implementation, as first or higher order ARMA filters in the time domain.

KW - Distributed time-varying computations

KW - graph filters

KW - graph Fourier transform

KW - signal processing on graphs

U2 - 10.1109/LSP.2015.2448655

DO - 10.1109/LSP.2015.2448655

M3 - Article

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SP - 1931

EP - 1935

JO - IEEE Signal Processing Letters

T2 - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

SN - 1070-9908

IS - 11

ER -

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