TY - JOUR

T1 - Distributed autoregressive moving average graph filters

AU - Loukas, Andreas

AU - Simonetto, Andrea

AU - Leus, Geert

PY - 2015

Y1 - 2015

N2 - 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.

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

VL - 22

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 -