Multi-snapshot Newtonized Orthogonal Matching Pursuit for Line Spectrum Estimation with Multiple Measurement Vectors

TL;DR

This paper introduces MNOMP, an efficient algorithm for line spectrum estimation with multiple measurement vectors, improving accuracy and reducing complexity compared to existing methods.

Contribution

The paper proposes MNOMP, a novel multi-snapshot algorithm that combines detection and refinement steps with proven convergence, outperforming state-of-the-art algorithms.

Findings

MNOMP benefits from multiple measurement vectors for improved accuracy.

MNOMP achieves lower computation complexity than existing methods.

Numerical results confirm MNOMP's superior frequency estimation performance.

Abstract

In this paper, multi-snapshot Newtonized orthogonal matching pursuit (MNOMP) algorithm is proposed to deal with the line spectrum estimation with multiple measurement vectors (MMVs). MNOMP has the low computation complexity and state-of-the-art performance advantage of NOMP, and also includes two key steps: Detecting a new sinusoid on an oversampled discrete Fourier transform (DFT) grid and refining the parameters of already detected sinusoids to avoid the problem of basis mismatch. We provide a stopping criterion based on the overestimating probability of the model order. In addition, the convergence of the proposed algorithm is also proved. Finally, numerical results are conducted to show that the performance of MNOMP benefits from MMVs, and the effectiveness of MNOMP when compared against the state-of-the-art algorithms in terms of frequency estimation accuracy and computation complexity.