Distributed Autoregressive Moving Average Graph Filters Article Swipe

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Autoregressive–moving-average model Mathematics Finite impulse response Infinite impulse response Graph Digital filter Voltage graph Algorithm Computer science Line graph Discrete mathematics Autoregressive model Filter (signal processing) Statistics Computer vision

Andreas Loukas , Andrea Simonetto , Geert Leus ·

YOU? · · 2015 · Open Access · · DOI: https://doi.org/10.1109/lsp.2015.2448655 · OA: W1937201840

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 ARMA filters in the time domain.