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Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

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Published on: June 25, 2021

Design a novel algorithm for enhancing UWB positioning accuracy in GPS denied environments.

Yuansheng Huang¹, Bo Cao², Ao Wang¹

¹School of Art Design, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang, 322100, China.

Scientific Reports

|October 14, 2024

Summary

Accurate indoor positioning is crucial for IoT devices. A new algorithm integrating Maximum Correntropy Criterion (MCC) and unscented Kalman filter (UKF) with Least Squares (LS) enhances ultra-wide band (UWB) system accuracy in GPS-denied environments.

Keywords:

GPS-denied environments Indoor positioning Localization accuracy Maximum Correntropy Criterion unscented Kalman filter (MCCUKF)Ultra-wide band (UWB)

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Published on: May 1, 2018

Area of Science:

Robotics and Automation
Signal Processing
Geomatics Engineering

Background:

Accurate indoor positioning is essential for the Internet of Things (IoT) and intelligent devices.
Global Positioning System (GPS) is ineffective in indoor environments, necessitating alternative positioning solutions.
Ultra-wide band (UWB) systems offer potential for precise indoor localization but are susceptible to noise and outliers.

Purpose of the Study:

To develop a novel algorithm for enhancing the localization accuracy of UWB systems in GPS-denied indoor environments.
To mitigate the impact of outliers and unknown process noise on UWB positioning accuracy.
To provide an effective positioning methodology for indoor IoT applications.

Main Methods:

Integration of Maximum Correntropy Criterion (MCC) and Unscented Kalman Filter (UKF) for measurement distance reconstruction.
Application of the maximum entropy principle to reduce outlier influence and process noise.
Utilizing Least Squares (LS) for initial position estimation and Taylor algorithm for optimization.

Main Results:

The developed MCCUKF-LS method significantly reduces Root Mean Square Error (RMSE) compared to LS, KF-LS, and UKF-LS methods.
Overall average RMSE reduced by 45.7% compared to the LS algorithm.
Achieved accuracy improvements in x-, y-, and z-axis orientation by 51.4%, 49.3%, and 55.1% respectively.

Conclusions:

The proposed MCCUKF-LS fusion technique effectively enhances the positioning accuracy of Target Nodes (TN) in indoor UWB systems.
The method demonstrates robustness against outliers and unknown process noise.
This provides a valuable new positioning methodology and reference for indoor localization in GPS-denied scenarios.