Near-Field Multi-Target Localization With Coprime Arrays

Large-aperture coprime arrays (CAs) are expected to achieve higher sensing resolution than conventional dense arrays (DAs), yet with lower hardware and energy cost. However, existing CA far-field localization methods cannot be directly applied to near-field scenarios due to channel model mismatch. To address this issue, in this paper, we propose an efficient near-field localization method for CAs. Specifically, we first construct an effective covariance matrix, which allows to decouple the target angle-and-range estimation. Then, a customized two-phase multiple signal classification (MUSIC) method for CAs is proposed, which first detects all possible angles of targets by using an angular-domain MUSIC method, followed by a second phase to resolve the true angles of targets and their ranges by devising a range-domain MUSIC method. We show that the proposed method can achieve near-optimal multi-target localization performance as conventional two-dimensional (2D)-MUSIC method with much lower computational complexity. Additionally, we characterize the Cramér-Rao bounds for symmetric CAs and provide interesting insights. Finally, numerical results demonstrate that our proposed method is able to localize more targets than the existing subarray-based method as well as achieve lower root mean square error than DAs.