A Gaussian Process-Enhanced Non-Linear Function and Bayesian Convolution–Bayesian Long Term Short Memory Based Ultra-Wideband Range Error Mitigation Method for Line of Sight and Non-Line of Sight Scenarios

A. S. M. Sharifuzzaman Sagar, Samsil Arefin, Eesun Moon, Md Masud Pervez Prince, L. Minh Dang, Amir Haider () and Hyung Seok Kim ()
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A. S. M. Sharifuzzaman Sagar: Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea
Samsil Arefin: School of Mechatronics Engineering, China University of Mining and Technology, Xuzhou 221116, China
Eesun Moon: Department of Computer Science, Columbia University, New York, NY 10027, USA
Md Masud Pervez Prince: Department of Industrial Design Engineering, Zhejiang University, Hangzhou 310058, China
L. Minh Dang: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Amir Haider: Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea
Hyung Seok Kim: Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2024, vol. 12, issue 23, 1-25

Abstract: Relative positioning accuracy between two devices is dependent on the precise range measurements. Ultra-wideband (UWB) technology is one of the popular and widely used technologies to achieve centimeter-level accuracy in range measurement. Nevertheless, harsh indoor environments, multipath issues, reflections, and bias due to antenna delay degrade the range measurement performance in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. This article proposes an efficient and robust method to mitigate range measurement error in LOS and NLOS conditions by combining the latest artificial intelligence technology. A GP-enhanced non-linear function is proposed to mitigate the range bias in LOS scenarios. Moreover, NLOS identification based on the sliding window and Bayesian Conv-BLSTM method is utilized to mitigate range error due to the non-line-of-sight conditions. A novel spatial–temporal attention module is proposed to improve the performance of the proposed model. The epistemic and aleatoric uncertainty estimation method is also introduced to determine the robustness of the proposed model for environment variance. Furthermore, moving average and min-max removing methods are utilized to minimize the standard deviation in the range measurements in both scenarios. Extensive experimentation with different settings and configurations has proven the effectiveness of our methodology and demonstrated the feasibility of our robust UWB range error mitigation for LOS and NLOS scenarios.

Keywords: error mitigation; Bayesian inference; deep learning; sensors; UWB (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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