On the Performance of One-Bit DoA Estimation via Sparse Linear Arrays

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