Line Spectral Analysis Using the G-Filter: An Atomic Norm Minimization Approach

TL;DR

This paper introduces a novel line spectral estimation method combining G-filter banks and atomic norm minimization, enabling efficient, single-output sinusoid detection without prior knowledge of the number of components.

Contribution

It develops a new atomic norm minimization approach using G-filters for line spectral analysis, generalizing existing methods and improving performance over traditional subspace techniques.

Findings

Performs favorably against standard ANM, frequency-selective ANM, MUSIC, and ESPRIT.

Works with only one output vector and no prior knowledge of sinusoid count.

Efficient semidefinite programming formulation for spectral estimation.

Abstract

The area of spectral analysis has a traditional dichotomy between continuous spectra (spectral densities) which correspond to purely nondeterministic processes, and line spectra (Dirac impulses) which represent sinusoids. While the former case is important in the identification of discrete-time linear stochastic systems, the latter case is essential for the analysis and modeling of time series with notable applications in radar systems. In this paper, we develop a novel approach for line spectral estimation which combines ideas of Georgiou's filter banks (G-filters) and atomic norm minimization (ANM), a mainstream method for line spectral analysis in the last decade following the theory of compressed sensing. Such a combination is only possible because a Carath\'{e}odory--Fej\'{e}r-type decomposition is available for the covariance matrix of the filter output. The ANM problem can be characterized via semidefinite programming which can be solved efficiently. As a consequence, our optimization scheme can be seen as a substantial generalization of the standard ANM for line spectral estimation. Moreover, our ANM approach with a G-filter has significant advantages over subspace methods because it can work with just one output vector and without \emph{a priori} knowledge about the number of sinusoids in the input. Simulation results show that our approach performs favorably against the standard ANM, the frequency-selective ANM, and standard subspace methods MUSIC and ESPRIT under a variety of parameter configurations when the G-filter is suitably designed.