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Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step

Hongchao Yang¹, Yunjia Wang¹, Shenglei Xu²

¹The Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China.

Sensors (Basel, Switzerland)

|March 13, 2024

Summary

This study introduces an efficient ultra-wideband (UWB) strategy to mitigate non-line-of-sight (NLOS) ranging errors for precise indoor positioning. The method significantly improves accuracy by identifying and correcting NLOS errors, outperforming existing techniques.

Keywords:

channel impulse response (CIR)deep learning indoor positioning and navigation non-line of sight (NLOS)ranging mitigation ultra-wideband (UWB)

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Area of Science:

Robotics and Automation
Signal Processing
Indoor Navigation

Background:

High-precision positioning in indoor environments is challenged by non-line-of-sight (NLOS) ranging errors.
Ultra-wideband (UWB) technology offers potential for accurate indoor localization but is susceptible to NLOS errors.

Purpose of the Study:

To propose an efficient strategy for identifying and mitigating NLOS ranging errors in UWB systems for improved indoor positioning.
To develop a two-step NLOS identification method combined with novel channel impulse response parameters for error correction.

Main Methods:

A two-step NLOS identification process using a decision tree and a feedforward neural network.
Classification of NLOS errors into three types with tailored mitigation and recall strategies.
Extension of mitigation strategies to partial line-of-sight (LOS) errors.

Main Results:

Achieved an average NLOS identification accuracy of 95.05% and LOS/NLOS recall rates of 95.72%/94.15%.
Reduced LOS errors by 50.4% and improved NLOS ranging error accuracy by an average of 61.8% (up to 76.84%).
Overall reduction in LOS and NLOS ranging errors by 25.19% and 69.85%, respectively, leading to a 54.46% enhancement in positioning accuracy.

Conclusions:

The proposed UWB ranging-error mitigation strategy effectively enhances indoor positioning accuracy.
This method demonstrates superior performance compared to state-of-the-art techniques like CNN, LSTM-EKF, LS-SVM, and K-NN.
The strategy provides a robust solution for precise localization in challenging indoor environments with NLOS conditions.