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

Bohao Wang, Zitao Shuai, Chongwen Huang, Qianqian Yang, Zhaohui Yang, Richeng Jin, Ahmed Al Hammadi, Zhaoyang Zhang, Chau Yuen, Mérouane Debbah

TL;DR

A novel multi-sources information fusion learning framework referred to as the Autosync Multi-Domains NLOS Localization (AMDNLoc), which 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.

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.

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

TL;DR

A novel multi-sources information fusion learning framework referred to as the Autosync Multi-Domains NLOS Localization (AMDNLoc), which 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.

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.
Paper Structure (23 sections, 14 equations, 14 figures, 5 tables, 2 algorithms)

This paper contains 23 sections, 14 equations, 14 figures, 5 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. AMDNLoc Multi-sources Framework
  4. Design and Application of a Heterogeneity Matched Filter
  5. PFCFR
  6. ADCAM
  7. Data Fusing and Cleansing
  8. Network Training
  9. Numerical Results
  10. Datasets
  11. Visualization of Classification Regions
  12. Effectiveness of Classification Methods
  13. Comparison of Multiple Processing Results (MPR)
  14. Comparison of Pre-processing on CFR or ADCAM
  15. Impact of System Parameters
  16. ...and 8 more sections

Figures (14)

  • Figure 1: System diagram of proposed AMDNLoc framework compared with existing methods.
  • Figure 2: The considered multiple MTs systems equipped with OFDM technology.
  • Figure 3: CFR example figure of randomly selected MTs in the 00743 scenario of WAIR-D
  • Figure 4: NLOS pre-classification in multi domains when $\tau _{in}=0.99, \tau _{out}=0.99$
  • Figure 5: NLOS pre-classification when change $\tau _{in}$ in frequency domain
  • ...and 9 more figures