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Due to the increasing demand for low power and higher sampling rates, lowresolution quantization for data acquisition has drawn great attentionrecently. Consequently, line spectral estimation (LSE) with multiplemeasurement vectors (MMVs) from coarsely quantized samples is of vitalimportance in cutting edge array signal processing applications such as rangeestimation 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 toestimate the frequencies from coarsely quantized samples. In addition, theCram\'{e}r Rao bound (CRB) is derived as a benchmark performance of theproposed algorithm, and insights are provided to reveal the effects of systemparameters on estimation performance. It is shown that snapshots benefits thefrequency estimation, especially in coarsely quantized scenarios. Numericalexperiments are conducted to demonstrate the effectiveness of MVALSE-EP,including real data set.

Gridless Multisnapshot Variational Line Spectral Estimation from Coarsely Quantized Samples