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A Robust Localization Algorithm Based on NLOS Identification and Classification Filtering for Wireless Sensor

Long Cheng¹, Sihang Huang¹, Mingkun Xue¹

¹Department of Computer and Communication Engineering, Northeastern University, Qinhuangdao 066004, China.

Sensors (Basel, Switzerland)

|November 24, 2020

Summary

This summary is machine-generated.

This study introduces a robust algorithm for wireless sensor network (WSN) localization that effectively handles non-line-of-sight (NLOS) errors. The method identifies and classifies NLOS severity, improving positioning accuracy in challenging environments.

Keywords:

NLOS identification NLOS localization classification filtering wireless sensor network

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Area of Science:

Computer Science
Electrical Engineering
Signal Processing

Background:

Wireless Sensor Networks (WSNs) are crucial for various applications, but their localization accuracy is hampered by Non-Line-of-Sight (NLOS) propagation.
Existing methods struggle with the complexity and variability of NLOS conditions, limiting positioning precision.

Purpose of the Study:

To develop a robust localization algorithm for WSNs that specifically addresses and mitigates NLOS errors.
To enhance positioning accuracy in WSNs by effectively identifying, classifying, and filtering NLOS noise.

Main Methods:

A novel NLOS identification strategy categorizes NLOS into mild and severe types.
Classification filtering employs a robust extended Kalman filter for mild NLOS and a LOS reconstruction algorithm for severe NLOS.
An interactive multiple model algorithm integrates Line-of-Sight (LOS) and NLOS estimates for final positioning.

Main Results:

The proposed algorithm effectively suppresses NLOS errors across different severity levels.
Achieved significantly higher positioning accuracy compared to existing WSN localization algorithms.
Demonstrated robustness in both simulation and experimental validation.

Conclusions:

The developed NLOS identification and classification filtering approach provides a robust solution for WSN localization.
This method offers a substantial improvement in positioning accuracy, making WSNs more reliable in complex environments.