Universal Graph Filter Design based on Butterworth, Chebyshev and Elliptic Functions

TL;DR

This paper introduces a universal method for designing low-complexity IIR graph filters using classical analog filter functions, achieving more accurate frequency responses than existing methods.

Contribution

It presents a novel approach to designing universal IIR graph filters based on Butterworth, Chebyshev, and Elliptic functions, inspired by classical analog filter design.

Findings

Proposed filters outperform ARMA, LLS, and Chebyshev FIR methods in accuracy.

Derived zeros and poles enable precise control of filter responses.

Constructed various filter types including low pass, band pass, and band stop.

Abstract

Graph filters are crucial tools in processing the spectrum of graph signals. In this paper, we propose to design universal IIR graph filters with low computational complexity by using three kinds of functions, which are Butterworth, Chebyshev, and Elliptic functions, respectively. Specifically, inspired by the classical analog filter design method, we first derive the zeros and poles of graph frequency responses. With these zeros and poles, we construct the conjugate graph filters to design the Butterworth high pass graph filter, Chebyshev high pass graph filter, and Elliptic high pass graph filter, respectively. On this basis, we further propose to construct a desired graph filter of low pass, band pass, and band stop by mapping the parameters of the desired graph filter to those of the equivalent high pass graph filter. Furthermore, we propose to set the graph filter order given the maximum passband attenuation and the minimum stopband attenuation. Our numerical results show that the proposed graph filter design methods realize the desired frequency response more accurately than the autoregressive moving average (ARMA) graph filter design method, the linear least-squares fitting (LLS) based graph filter design method, and the Chebyshev FIR polynomial graph filter design method.