Mixed Near-field and Far-field Target Localization for Low-altitude Economy

In this paper, we study efficient mixed near-field and far-field target localization methods for low-altitude economy, by capitalizing on extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems. Compared with existing works, we address three new challenges in localization, arising from 1) half-wavelength antenna spacing constraint, 2) hybrid uniform planar array (UPA) architecture, and 3) incorrect mixed-field target classification for near-field targets.To address these issues, we propose a new three-step mixed-field localization method.First, we reconstruct the signals received at UPA antennas by judiciously designing analog combining matrices over time with minimum recovery errors, thus tackling the reduced-dimensional signal-space issue in hybrid arrays.Second, based on recovered signals, we devise a modified MUSIC algorithm (catered to UPA architecture) to estimate 2D angular parameters of both far- and near-field targets. Due to half-wavelength inter-antenna spacing, there exist ambiguous angles when estimating true angles of targets.In the third step, we design an effective classification method to distinguish mixed-field targets, determine true angles of all targets, as well as estimate the ranges of near-field targets. In particular, angular ambiguity is resolved by showing an important fact that the three types of estimated angles (i.e., far-field, near-field, and ambiguous angles) exhibit significantly different patterns in the range-domain MUSIC spectrum. Furthermore, to characterize the estimation error lower-bound, we obtain a matrix closed-form Cram\'er-Rao bounds for mixed-field target localization. Finally, numerical results demonstrate the effectiveness of our proposed mixed-field localization method, which improves target-classification accuracy and achieves a lower root mean square error than various benchmark schemes.

Near-Field Localization With Coprime Array

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) algorithm for CAs is proposed, which first detects all possible targets' angles by using an angular-domain MUSIC algorithm, followed by the second phase to resolve the true targets' angles and ranges by devising a range-domain MUSIC algorithm. Finally, we show that the proposed method is able to locate more targets than the subarray-based method as well as achieve lower root mean square error (RMSE) than DAs.