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

TL;DR

This paper introduces a machine learning approach for near-field emitter localization using a grouped hybrid analog-digital XL-MIMO array, addressing phase ambiguity and improving accuracy with low complexity.

Contribution

It proposes a grouped PC-HAD structure for near-field localization, along with ML-based methods to eliminate phase ambiguity and enhance performance.

Findings

RegNet outperforms at low SNR

Clustering methods offer lower complexity

CRLB analysis shows performance trade-offs

Abstract

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 obtain the exact NF DOA estimation results, we propose a grouped PC-HAD structure, which is capable of dividing the NF DOA estimation problem into multiple FF DOA estimation problems via 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) of NF emitter localization for the proposed grouped PC-HAD structure is derived and reveals that localization performance will decrease with the increasing of the number of groups. The simulation results show that the proposed methods can achieve CRLB at different SNR regions, the RegNet has great performance advantages at low SNR regions and the clustering-based methods have much lower computation complexity.