Ranked Sparse Signal Support Detection

TL;DR

This paper analyzes a simplified orthogonal matching pursuit algorithm for detecting sparse signals, highlighting how ranking components by conditional power improves detection performance and approaches optimality at high SNR.

Contribution

It introduces the SequOMP algorithm that leverages ranking of conditional powers to enhance sparse support detection, reducing the impact of dynamic range effects.

Findings

SequOMP performance improves with correct ranking of components.

Ranking by conditional power eliminates dynamic range effects.

At high SNR, SequOMP approaches maximum likelihood detection.

Abstract

This paper considers the problem of detecting the support (sparsity pattern) of a sparse vector from random noisy measurements. Conditional power of a component of the sparse vector is defined as the energy conditioned on the component being nonzero. Analysis of a simplified version of orthogonal matching pursuit (OMP) called sequential OMP (SequOMP) demonstrates the importance of knowledge of the rankings of conditional powers. When the simple SequOMP algorithm is applied to components in nonincreasing order of conditional power, the detrimental effect of dynamic range on thresholding performance is eliminated. Furthermore, under the most favorable conditional powers, the performance of SequOMP approaches maximum likelihood performance at high signal-to-noise ratio.