# Gridless Multisnapshot Variational Line Spectral Estimation from Coarsely Quantized Samples

## TL;DR

This paper introduces a low-complexity, gridless variational line spectral estimation method combined with expectation propagation for estimating frequencies from coarsely quantized samples, improving performance in array signal processing.

## Contribution

It proposes the MVALSE-EP algorithm that effectively estimates frequencies from low-resolution data, incorporating a CRB benchmark and analyzing parameter effects.

## Key findings

- Snapshots improve frequency estimation accuracy.
- The proposed method outperforms existing techniques in coarsely quantized scenarios.
- Numerical experiments validate the effectiveness of MVALSE-EP, including real data.

## Abstract

Due to the increasing demand for low power and higher sampling rates, low resolution quantization for data acquisition has drawn great attention recently. Consequently, line spectral estimation (LSE) with multiple measurement vectors (MMVs) from coarsely quantized samples is of vital importance in cutting edge array signal processing applications such as range estimation and DOA estimation in millimeter wave radar systems. In this paper, we combine the low complexity gridless variational line spectral estimation (VALSE) and expectation propagation (EP) and propose an MVALSE-EP algorithm to estimate the frequencies from coarsely quantized samples. In addition, the Cram\'{e}r Rao bound (CRB) is derived as a benchmark performance of the proposed algorithm, and insights are provided to reveal the effects of system parameters on estimation performance. It is shown that snapshots benefits the frequency estimation, especially in coarsely quantized scenarios. Numerical experiments are conducted to demonstrate the effectiveness of MVALSE-EP, including real data set.