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Foreign Object Intrusion Detection on Metro Track Using Commodity WiFi Devices with the Fast Phase Calibration

Shuo Li¹, Jin Xie¹, Feng Zhou¹

¹School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China.

Sensors (Basel, Switzerland)

|June 24, 2020

Summary

This study introduces a WiFi-based method for detecting foreign objects in train tunnels. The system uses radio frequency signals for accurate detection and localization, enhancing railway safety.

Keywords:

CSI WiFi angle-of-arrival foreign objects intrusion detection indoor localization phase calibration

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Area of Science:

Engineering
Computer Science
Transportation Science

Background:

Urban metro systems are crucial for transportation, but foreign objects in tunnels pose significant safety risks.
Existing detection methods may be limited in dark, complex tunnel environments.

Purpose of the Study:

To develop and evaluate a novel foreign object intrusion detection system for high-speed trains in tunnels using WiFi technology.
To improve the safety and efficiency of metro operations by enabling reliable detection and localization of foreign objects.

Main Methods:

A fast phase calibration method is proposed, compensating for channel state information (CSI) phase offsets using azimuth data.
A foreign object detection and localization method combines the MUSIC algorithm with static clutter suppression, leveraging signal incoherence.
Experiments were conducted using Intel 5300 NIC in a simulated tunnel environment to assess performance.

Main Results:

The proposed phase calibration method offers lower time complexity compared to existing algorithms.
The combined MUSIC algorithm and clutter suppression effectively improves foreign object detection and localization in multipath environments.
Experimental results demonstrate the method's capability in terms of detection probability and localization accuracy.

Conclusions:

The developed WiFi-based foreign object detection system is effective for lightless tunnel environments.
The proposed methods enhance detection accuracy and localization capabilities, contributing to improved railway safety.
This technology offers a promising solution for ensuring the safety of high-speed trains in urban tunnels.