  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.
