Multi-Sources Fusion Learning for Multi-Points NLOS Localization in OFDM System

TL;DR

This paper introduces AMDNLoc, a novel multi-source fusion learning framework that significantly improves NLOS localization accuracy in OFDM systems by automatically segmenting regions and correlating features with coordinates.

Contribution

The paper presents a new multi-sources information fusion framework with automatic region segmentation and deep learning-based feature correlation for NLOS localization in OFDM systems.

Findings

Achieves 1.46 meters localization accuracy in simulations

Improves interpretability, adaptability, and scalability of NLOS localization methods

Demonstrates significant performance gains over traditional fingerprint-based approaches

Abstract

Accurate localization of mobile terminals is a pivotal aspect of integrated sensing and communication systems. Traditional fingerprint-based localization methods, which infer coordinates from channel information within pre-set rectangular areas, often face challenges due to the heterogeneous distribution of fingerprints inherent in non-line-of-sight (NLOS) scenarios, particularly within orthogonal frequency division multiplexing systems. To overcome this limitation, we develop a novel multi-sources information fusion learning framework referred to as the Autosync Multi-Domains NLOS Localization (AMDNLoc). Specifically, AMDNLoc employs a two-stage matched filter fused with a target tracking algorithm and iterative centroid-based clustering to automatically and irregularly segment NLOS regions, ensuring uniform distribution within channel state information across frequency, power, and time-delay domains. Additionally, the framework utilizes a segment-specific linear classifier array, coupled with deep residual network-based feature extraction and fusion, to establish the correlation function between fingerprint features and coordinates within these regions. Simulation results reveal that AMDNLoc achieves an impressive NLOS localization accuracy of 1.46 meters on typical wireless artificial intelligence research datasets and demonstrates significant improvements in interpretability, adaptability, and scalability.