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Published on: October 27, 2016

LRF-WiVi: A WiFi and Visual Indoor Localization Method Based on Low-Rank Fusion.

Wen Liu¹, Changyan Qin¹, Zhongliang Deng¹

¹School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)

|November 26, 2022

Summary

This study introduces LRF-WiVi, a WiFi and visual fingerprint localization model. It effectively fuses heterogeneous signals for improved indoor positioning accuracy by leveraging their complementary nature.

Keywords:

WiFi channel state information end-to-end fingerprint localization low rank fusion visual images

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Area of Science:

Computer Science
Electrical Engineering
Robotics

Background:

Accurate indoor positioning remains a challenge, especially in complex environments.
Existing methods often struggle to effectively integrate heterogeneous sensor data.

Purpose of the Study:

To propose a novel WiFi and visual fingerprint localization model, LRF-WiVi.
To enhance indoor positioning accuracy by exploiting the complementarity of WiFi and visual signals.

Main Methods:

Developed two feature extraction subnetworks for WiFi Channel State Information (CSI) and multi-directional visual images.
Implemented a low-rank fusion module for efficient aggregation of feature vectors.
Designed a novel CSI time-frequency characteristic map construction and a double-branch CNN for CSI feature extraction.

Main Results:

The LRF-WiVi model demonstrated superior performance in both complex laboratory and open hall scenarios.
Extensive experiments verified the model's effectiveness in utilizing WiFi and visual signal complementarity.
Achieved more advanced positioning performance compared to existing methods.

Conclusions:

LRF-WiVi effectively fuses WiFi and visual data for robust indoor localization.
The proposed method offers a significant advancement in utilizing heterogeneous signal complementarity for positioning.