  Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has
recently gained considerable attention in array processing thanks to their
capability to provide enhanced degrees of freedom in resolving uncorrelated
source signals. Additionally, deployment of one-bit Analog-to-Digital
Converters (ADCs) has emerged as an important topic in array processing, as it
offers both a low-cost and a low-complexity implementation. In this paper, we
study the problem of DoA estimation from one-bit measurements received by an
SLA. Specifically, we first investigate the identifiability conditions for the
DoA estimation problem from one-bit SLA data and establish an equivalency with
the case when DoAs are estimated from infinite-bit unquantized measurements.
Towards determining the performance limits of DoA estimation from one-bit
quantized data, we derive a pessimistic approximation of the corresponding
Cram\'{e}r-Rao Bound (CRB). This pessimistic CRB is then used as a benchmark
for assessing the performance of one-bit DoA estimators. We also propose a new
algorithm for estimating DoAs from one-bit quantized data. We investigate the
analytical performance of the proposed method through deriving a closed-form
expression for the covariance matrix of the asymptotic distribution of the DoA
estimation errors and show that it outperforms the existing algorithms in the
literature. Numerical simulations are provided to validate the analytical
derivations and corroborate the resulting performance improvement.
