# Rank-one Multi-Reference Factor Analysis

## TL;DR

This paper develops methods for accurately estimating signals from noisy, cyclically-shifted observations in low SNR regimes, with proven consistency and improved sample complexity, supported by numerical experiments.

## Contribution

It introduces a novel consistent estimation procedure for low SNR, cyclically-shifted data, and demonstrates improved sample complexity through a new algorithm.

## Key findings

- Estimation is possible in low SNR regimes with a sample complexity of 1/SNR^4.
- Proposed procedure improves sample complexity by a factor equal to the signal length.
- Numerical experiments validate theoretical results and algorithm performance.

## Abstract

In recent years, there is a growing need for processing methods aimed at extracting useful information from large datasets. In many cases the challenge is to discover a low-dimensional structure in the data, often concealed by the existence of nuisance parameters and noise. Motivated by such challenges, we consider the problem of estimating a signal from its scaled, cyclically-shifted and noisy observations. We focus on the particularly challenging regime of low signal-to-noise ratio (SNR), where different observations cannot be shift-aligned. We show that an accurate estimation of the signal from its noisy observations is possible, and derive a procedure which is proved to consistently estimate the signal. The asymptotic sample complexity (the number of observations required to recover the signal) of the procedure is $1/\operatorname{SNR}^4$. Additionally, we propose a procedure which is experimentally shown to improve the sample complexity by a factor equal to the signal's length. Finally, we present numerical experiments which demonstrate the performance of our algorithms, and corroborate our theoretical findings.