Machine Learning-Based Near-Field Emitter Location Sensing via Grouped Hybrid Analog and Digital XL-MIMO Receive Array

As a green MIMO structure, the partially-connected hybrid analog and digital (PC-HAD) structure has been widely used in the far-field (FF) scenario for it can significantly reduce the hardware cost and complexity of large-scale or extremely large-scale MIMO (XL-MIMO) array. Recently, near-field (NF) emitter localization including direction-of-arrival (DOA) and range estimations has drawn a lot of attention, but is rarely explored via PC-HAD structure. In this paper, we first analyze the impact of PC-HAD structure on the NF emitter localization and observe that the phase ambiguity (PA) problem caused by PC-HAD structure can be removed inherently with low-latency in the NF scenario. To achieve accurate NF DOA estimation, we propose a grouped PC-HAD structure that decomposes the NF DOA estimation problem into multiple FF DOA estimation subproblems by partitioning the large-scale PC-HAD array into small-scale groups. An angle calibration method is developed to address the inconsistency among these FF DOA estimation problems. Then, to eliminate PA and improve the NF emitter localization performance, we develop three machine learning (ML)-based methods, i.e., two low-complexity data-driven clustering-based methods and one model-driven regression method, namely RegNet. Furthermore, the Cramer-Rao lower bound (CRLB) for NF emitter localization using the proposed grouped PC-HAD structure is derived, revealing that localization performance degrades as the number of groups increases. The simulation results show that the proposed methods can achieve CRLB at different SNR regions, the RegNet exhibits superior performance in low-SNR regions, while the clustering-based methods offer significantly lower computational complexity.