Filter Design for Autoregressive Moving Average Graph Filters

Filter Design for Autoregressive Moving Average Graph Filters

Liu, J. (TU Delft Signal Processing Systems)
Isufi, E. (TU Delft Signal Processing Systems)
Leus, G.J.T. (TU Delft Signal Processing Systems)

2018

In the field of signal processing on graphs, graph filters play a crucial role in processing the spectrum of graph signals. This paper proposes two different strategies for designing autoregressive moving average (ARMA) graph filters on both directed and undirected graphs. The first approach is inspired by Prony's method, which considers a modified error between the modeled and the desired frequency response. The second technique is based on an iterative approach, which finds the filter coefficients by iteratively minimizing the true error (instead of the modified error) between the modeled and the desired frequency response. The performance of the proposed algorithms is evaluated and compared with finite impulse response (FIR) graph filters, on both synthetic and real data. The obtained results show that ARMA filters outperform FIR filters in terms of approximation accuracy and they are suitable for graph signal interpolation, compression and prediction.

autoregressive moving average graph filters
iterative processing
Pronys method
Signal processing on graphs

http://resolver.tudelft.nl/uuid:9688b047-630c-4c6f-a474-5f167a0736d3

https://doi.org/10.1109/TSIPN.2018.2854627

2019-10-01

2373-776X

IEEE Transactions on Signal and Information Processing over Networks, 5 (2019) (1), 47 - 60

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2018 J. Liu, E. Isufi, G.J.T. Leus