Compressed Sensing-Driven Near-Field Localization Exploiting Array of Subarrays

2026-01-29

AI Summary

This work addresses the challenges of near-field localization with large-scale antenna arrays, where fully digital implementations incur prohibitive costs and sparse subarrays suffer from severe angular ambiguity due to grating lobes. To overcome these limitations, the authors propose SHARE, a hierarchical sparse recovery framework. In the first stage, unambiguous coarse angle estimates are obtained from individual subarrays to resolve grating lobe ambiguities; in the second stage, a joint angle-range search is performed over a localized region using the full sparse aperture, thereby avoiding exhaustive two-dimensional grid searches. The approach effectively balances high resolution—enabled by a large effective aperture—with low computational complexity. Simulations demonstrate that SHARE significantly outperforms conventional methods such as OMP in both localization accuracy and robustness, achieving performance comparable to or even surpassing that of 2D-MUSIC, which relies on full-array data.

Technology Category

• Search and Optimization: Local Search
• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Near-field localization for ISAC requires large-aperture arrays, making fully-digital implementations prohibitively complex and costly. While sparse subarray architectures can reduce cost, they introduce severe estimation ambiguity from grating lobes. To address both issues, we propose SHARE (Sparse Hierarchical Angle-Range Estimation), a novel two-stage sparse recovery algorithm. SHARE operates in two stages. It first performs coarse, unambiguous angle estimation using individual subarrays to resolve the grating lobe ambiguity. It then leverages the full sparse aperture to perform a localized joint angle-range search. This hierarchical approach avoids an exhaustive and computationally intensive two-dimensional grid search while preserving the high resolution of the large aperture. Simulation results show that SHARE significantly outperforms conventional one-shot sparse recovery methods, such as Orthogonal Matching Pursuit (OMP), in both localization accuracy and robustness. Furthermore, we show that SHARE's overall localization accuracy is comparable to or even surpasses that of the fully-digital 2D-MUSIC algorithm, despite MUSIC having access to the complete, uncompressed data from every antenna element. SHARE therefore provides a practical path for high-resolution near-field ISAC systems.

Problem

• Near-field localization
• ISAC
• Sparse arrays
• Grating lobes
• High resolution

Innovation

• Compressed Sensing
• Near-field Localization
• Sparse Array
• Hierarchical Estimation
• ISAC