Source Localization for Extremely Large-Scale Antenna Arrays Under Spatial Non-Stationarity and Near-Field Effects

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Authors: Xiaohuan Wu, Jin Qiu, Ji Sun, Wei Liu, Haiyang Zhang, Yonina C. Eldar
Cited by: 3
References: 47

Abstract

To achieve ultra-high precision positioning, the extremely large-scale antenna array (ELAA), consisting of hundreds or even thousands of antenna elements, has garnered significant attention. However, due to increased antenna aperture, it inevitably encounters both near-field effects and spatial non-stationarity effects. In the near-field region, the traditional assumption of far-field plane wavefront no longer holds, necessitating consideration of spherical wave characteristics. Spatial non-stationarity arises when signals fail to reach the entire array, but instead only impinge on a subset of antennas, which is referred to as the signal’s visible region (VR). Both effects cause model mismatch and therefore reduce positioning accuracy. In this paper, we introduce an exact near-field signal model in the context of ELAA. Based on this model, we prove that the steering vectors of source signals and the eigenvectors of the signal subspace become collinear as the number of antennas approaches infinity, which makes it easier to estimate the VR and source location parameters. Accordingly, we develop an estimation method to effectively extract the VR information of signals even when the VRs are discontinuous or overlapping. After obtaining the VR information, we propose three source localization methods that leverage the estimated VR and eigenvectors. Simulation results demonstrate that the proposed methods achieve high-precision localization while reducing computational complexity, thereby overcoming the model mismatch induced by near-field effects and spatial non-stationarity effects in ELAA.

Fields of Study & Subjects

Direction-of-Arrival Estimation Techniques Advanced SAR Imaging Techniques Radar Systems and Signal Processing

Publication details

Journal / source
IEEE Transactions on Signal Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Volume
74
Pages
685–700
Publication date
1 January 2026