# Near Field Localization via AI-Aided Subspace Methods

arXiv: https://arxiv.org/abs/2504.00599 · HTML: https://mlsanity.com/paper/2504.00599/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 86 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 28 of 28)

- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound](https://mlsanity.com/paper/1811.08252/) (2018)
- [A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence](https://mlsanity.com/paper/2006.12567/) (2020)
- [A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications](https://mlsanity.com/paper/2307.07340/) (2023)
- [Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends](https://mlsanity.com/paper/1911.12296/) (2019)
- [Deep Networks for Direction-of-Arrival Estimation in Low SNR](https://mlsanity.com/paper/2011.08848/) (2020)
- [Adaptive Ultrasound Beamforming using Deep Learning](https://mlsanity.com/paper/1909.10342/) (2019)
- [Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals](https://mlsanity.com/paper/2304.07827/) (2023)
- [Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications](https://mlsanity.com/paper/2006.07838/) (2020)
- [Discriminative and Generative Learning for Linear Estimation of Random Signals [Lecture Notes]](https://mlsanity.com/paper/2206.04432/) (2022)
- [DeepMUSIC: Multiple Signal Classification via Deep Learning](https://mlsanity.com/paper/1912.04357/) (2019)
- [DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm](https://mlsanity.com/paper/2109.10581/) (2021)
- [Beam Focusing for Near-Field Multi-User MIMO Communications](https://mlsanity.com/paper/2105.13087/) (2021)
- [SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation](https://mlsanity.com/paper/2306.02271/) (2023)
- [Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication under Hardware Impairments](https://mlsanity.com/paper/2307.04111/) (2023)
- [A Tutorial on Near-Field XL-MIMO Communications Towards 6G](https://mlsanity.com/paper/2310.11044/) (2023)
- [Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays](https://mlsanity.com/paper/2411.15144/) (2024)
- [TransMUSIC: A Transformer-Aided Subspace Method for DOA Estimation with Low-Resolution ADCs](https://mlsanity.com/paper/2309.08174/) (2023)
- [Massive MIMO channel estimation taking into account spherical waves](https://mlsanity.com/paper/1811.05669/) (2018)
- [Complex ResNet Aided DoA Estimation for Near-Field MIMO Systems](https://mlsanity.com/paper/2007.10590/) (2020)
- [Near-field Tracking with Large Antenna Arrays: Fundamental Limits and Practical Algorithms](https://mlsanity.com/paper/2102.05890/) (2021)
- [6G Wireless Communications: From Far-field Beam Steering to Near-field Beam Focusing](https://mlsanity.com/paper/2203.13035/) (2022)
- [Towards 6G Holographic Localization: Enabling Technologies and Perspectives](https://mlsanity.com/paper/2103.12415/) (2021)
- [Unsupervised Parameter Estimation using Model-based Decoder](https://mlsanity.com/paper/2211.01849/) (2022)
- [Beam Focusing for Near-Field Multi-User Localization](https://mlsanity.com/paper/2407.20264/) (2024)
- [Near-field Localization with Dynamic Metasurface Antennas](https://mlsanity.com/paper/2210.16085/) (2022)
- [Near-Field Localization with Antenna Arrays in the Presence of Direction-Dependent Mutual Coupling](https://mlsanity.com/paper/2408.07202/) (2024)

## Built on by (most cited first, 1 of 1)

- [Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC](https://mlsanity.com/paper/2609.09409/) (2026)

## Abstract (arXiv)

The increasing demands for high-throughput and energy-efficient wireless communications are driving the adoption of extremely large antennas operating at high-frequency bands. In these regimes, multiple users will reside in the radiative near-field, and accurate localization becomes essential. Unlike conventional far-field systems that rely solely on DOA estimation, near-field localization exploits spherical wavefront propagation to recover both DOA and range information. While subspace-based methods, such as 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 AI-aided subspace methods for near-field localization that enhance robustness to real-world challenges. Specifically, we introduce NF-SubspaceNet, a deep learning-augmented 2D MUSIC algorithm that learns a surrogate covariance matrix to improve localization under challenging conditions, and DCD-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.