Off-the-grid Recovery of Time and Frequency Shifts with Multiple Measurement Vectors

TL;DR

This paper introduces a semidefinite programming method for off-the-grid recovery of multiple time and frequency shifts from limited measurements, improving sample efficiency and separation conditions in radar and wireless applications.

Contribution

It proposes a novel semidefinite programming approach for exact recovery of multiple continuous-valued time-frequency shifts from MMVs, with theoretical guarantees and improved sampling conditions.

Findings

Exact recovery of s pairs from L samples under separation conditions

Linear scaling of the number of shifts with samples, up to a log factor

Reduced minimum separation requirement compared to previous methods

Abstract

We address the problem of estimating time and frequency shifts of a known waveform in the presence of multiple measurement vectors (MMVs). This problem naturally arises in radar imaging and wireless communications. Specifically, a signal ensemble is observed, where each signal of the ensemble is formed by a superposition of a small number of scaled, time-delayed, and frequency shifted versions of a known waveform sharing the same continuous-valued time and frequency components. The goal is to recover the continuous-valued time-frequency pairs from a small number of observations. In this work, we propose a semidefinite programming which exactly recovers $s$ pairs of time-frequency shifts from $L$ regularly spaced samples per measurement vector under a minimum separation condition between the time-frequency shifts. Moreover, we prove that the number $s$ of time-frequency shifts scales linearly with the number $L$ of samples up to a log-factor. Extensive numerical results are also provided to validate the effectiveness of the proposed method over the single measurement vectors (SMVs) problem. In particular, we find that our approach leads to a relaxed minimum separation condition and reduced number of required samples.