AUTOREGRESSIVE MOVING AVERAGE GRAPH FILTERS A STABLE DISTRIBUTED IMPLEMENTATION

We present a novel implementation strategy for distributed autoregressive moving average (ARMA) graph filters. Differently from the state of the art implementation, the proposed approach has the following benefits: (i) the designed filter coefficients come with stability guarantees, (ii) the linear convergence time can now be controlled by the filter coefficients, and (iii) the stable filter coefficients that approximate a desired frequency response are optimal in a least squares sense. Numerical results show that the proposed implementation outperforms the state of the art distributed infinite impulse response (IIR) graph filters. Further, even at fixed distributed costs, compared with the popular finite impulse response (FIR) filters, at high orders our method achieves tighter low-pass responses, suggesting that it should be preferable in accuracy-demanding applications.


Published in:
2017 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp), 4119-4123
Presented at:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA
Year:
2017
Publisher:
New York, Ieee
ISSN:
1520-6149
ISBN:
978-1-5090-4117-6
Keywords:
Laboratories:




 Record created 2016-12-22, last modified 2018-01-28


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