One-Bit Underdetermined DOA Estimation with Sparse Arrays via Structured Covariance Reconstruction: Invited Paper

Recently, one-bit direction of arrival (DOA) estimation has received significant attention due to its low cost and low implementation complexity, while still achieving high accuracy without the need of high-resolution measurements. In this work, we consider nonlinear estimation errors under finite number of snapshots in one-bit covariance reconstruction, and propose the two-step reconstruction approach, first using the arcsine law to reconstruct the unquantized covariance matrix and then incorporating the Toeplitz Hermitian structure as prior information to reconstruct the full-scale virtual uniform linear array (ULA) covariance matrix. It is shown that the error is smaller than the case when the two steps are swapped in order. Simulation results demonstrate the large difference in performance due to the order in which the two steps are applied, our proposed method has remarkably outperformed the current state-of-the-art solutions.