Distributed autoregressive moving average graph filters

Journal article (2015)

Authors

A. Loukas Embedded Systems - EEMCS
A. Simonetto Signal Processing Systems - EEMCS
G.J.T. Leus Signal Processing Systems - EEMCS
Research Group
Embedded Systems (EEMCS) (TU Delft)
Signal processing on graphs Graph filters Graph Fourier transform Distributed time-varying computations
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Published Date

2015

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Embedded Systems

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.