Next Article in Journal
Interval Estimation for the Two-Parameter Exponential Distribution Based on Upper Record Value Data Using Bayesian Approaches
Next Article in Special Issue
Preface to the Special Issue “Modeling and Simulation in Engineering, 3rd Edition”
Previous Article in Journal
A Unified Graph Theory Approach: Clustering and Learning in Criminal Data
Previous Article in Special Issue
Second-Order Terminal Sliding Mode Control for Trajectory Tracking of a Differential Drive Robot
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Mathematics
Volume 12
Issue 23
10.3390/math12233866
mathematics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
A. S. M. Sharifuzzaman Sagar
1,
Samsil Arefin
2,
Eesun Moon
3,
Md Masud Pervez Prince
4,
L. Minh Dang
5,6,
Amir Haider
1,* and
Hyung Seok Kim
1,*
1
Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea
2
School of Mechatronics Engineering, China University of Mining and Technology, Xuzhou 221116, China
3
Department of Computer Science, Columbia University, New York, NY 10027, USA
4
Department of Industrial Design Engineering, Zhejiang University, Hangzhou 310058, China
5
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
6
Faculty of Information Technology, Duy Tan University, Da Nang 550000, Vietnam
*
Authors to whom correspondence should be addressed.
Mathematics 2024, 12(23), 3866; https://doi.org/10.3390/math12233866
Submission received: 29 October 2024 / Revised: 28 November 2024 / Accepted: 1 December 2024 / Published: 9 December 2024
(This article belongs to the Special Issue Modeling and Simulation in Engineering, 3rd Edition)
Browse Figures
Versions Notes

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 error mitigation; Bayesian inference; deep learning; sensors; UWB

Share and Cite

MDPI and ACS Style

Sagar, A.S.M.S.; Arefin, S.; Moon, E.; Prince, M.M.P.; Dang, L.M.; Haider, A.; Kim, H.S. 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. Mathematics 2024, 12, 3866. https://doi.org/10.3390/math12233866

AMA Style

Sagar ASMS, Arefin S, Moon E, Prince MMP, Dang LM, Haider A, Kim HS. 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. Mathematics. 2024; 12(23):3866. https://doi.org/10.3390/math12233866

Chicago/Turabian Style

Sagar, A. S. M. Sharifuzzaman, Samsil Arefin, Eesun Moon, Md Masud Pervez Prince, L. Minh Dang, Amir Haider, and Hyung Seok Kim. 2024. "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" Mathematics 12, no. 23: 3866. https://doi.org/10.3390/math12233866

APA Style

Sagar, A. S. M. S., Arefin, S., Moon, E., Prince, M. M. P., Dang, L. M., Haider, A., & Kim, H. S. (2024). 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. Mathematics, 12(23), 3866. https://doi.org/10.3390/math12233866

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop