In systems operating with extremely large antenna arrays and high-frequency signaling, multiple users often reside in the radiative near-field, and accurate localization becomes essential. Unlike conventional far-field systems that rely solely on direction of arrival (DoA) estimation, near-field localization exploits spherical wavefront propagation to recover both DoA and range information. While subspace-based methods, such as MUltiple SIgnal Classification (MUSIC) and its extensions, offer high resolution and interpretability for near-field localization, their performance is significantly impacted by model assumptions, including non-coherent sources, well-calibrated arrays, and a sufficient number of snapshots. To address these limitations, this work proposes artificial intelligence (AI)-aided subspace methods for near-field localization that enhance robustness to real-world challenges. Specifically, we introduceNF-SubspaceNet, a deep learning-augmented 2D MUSIC algorithm that learns a surrogate covariance matrix to improve localization under challenging conditions, andDCD-MUSIC, a cascaded AI-aided approach that decouples angle and range estimation to reduce computational complexity. We further develop a novel model-order-aware training method to accurately estimate the number of sources, that is combined with casting of near-field subspace methods as AI models for learning. Extensive simulations demonstrate that the proposed methods outperform classical and existing deep-learning-based localization techniques, providing robust near-field localization even under coherent sources, miscalibrations, and few snapshots.

Future wireless technologies will require accurate localization of multiple users in the radiative near-field. A leading approach employs subspace decomposition of the input covariance and localizes by peak-finding over the MUltiple SIgnal Classification (MUSIC) spectrum, which is suitable for non-coherent sources with sufficient snapshots and calibrated arrays. This work introduces deep-learning-aided cascaded differentiable MUSIC (DCD-MUSIC) that augments MUSIC near-field localization with dedicated deep neural networks (DNNs), allowing it to operate reliably and interpretably. DCD-MUSIC utilizes two DNNs trained to produce surrogate covariances, one from which the angles and number of sources are recovered, and one to compute the range MUSIC spectrum. This is achieved via a novel learning method that (i) facilitates division into signal and noise subspaces; and (ii) converts MUSIC into a differentiable machine learning model. Our results show that DCD-MUSIC successfully localizes multiple coherent near- and far-field sources.