On Mitigation of Ranging Errors for Through-the-Body NLOS Conditions using Convolutional Neural Networks

Nam, Sung-Chan; Choi, Hong-Beom; Ko, Young-Bae

doi:10.23919/icact51234.2021.9370584

2021 23rd International Conference on Advanced Communication Technology (ICACT) 2021

DOI: 10.23919/icact51234.2021.9370584

|View full text |Cite

On Mitigation of Ranging Errors for Through-the-Body NLOS Conditions using Convolutional Neural Networks

Sung-Chan Nam

Hong-Beom Choi

Young-Bae Ko

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A comparative study on NLOS error elimination methods based on channel measurement experiment

Chang

Wang²,

GAO³

et al. 2022

西北工业大学学报

View full text Add to dashboard Cite

In order to study the performance of different elimination methods on the distance estimation forward error caused by the non-line-of-sight (NLOS) propagation of radio signals, this paper is based on the mean value, root mean square delay spread, skewness, kurtosis and peak-to-average ratio extracted from the channel state information (CSI), and combine it with the logarithmic estimated distance based on the time of arrival (TOA) as the feature input vector, through the establishment of Gaussian process regression (GPR), least square support vector machine regression (LS-SVMR) and BP neural network training model for experimental performance comparison. Through the actual measurement of the 2.4 to 5.4 GHz wireless propagation channel in the typical indoor environment, the error elimination experiment is carried out to compare the NLOS error elimination performance under different input characteristics, different bandwidths and different frequency bands. The experimental results show that the GPR model has the best NLOS error elimination performance, and the extracted CSI multi-features as the input of the GPR model can reduce the average absolute error and root mean square error by 71.12% and 81.36%, respectively. As the bandwidth continues to increase, the error elimination performance is gradually optimized. By increasing the bandwidth, the NLOS positioning error when the input features are less can be effectively improved. The positioning error of the low frequency band is smaller than that of the high frequency band under the multi-features, so the combination of all available frequency bands can eliminate the NLOS positioning error better than a single frequency band.

show abstract

A comparative study on NLOS error elimination methods based on channel measurement experiment

Chang

Wang²,

GAO³

et al. 2022

西北工业大学学报

View full text Add to dashboard Cite

show abstract

UWB distance estimation errors in (non-)line of sight situations within the context of 3D analysis of human movement

Yogesh,

Rook,

Keizers

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

Integrated Ultrawideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensors are becoming popular for indoor localization applications, as a higher accuracy can be achieved than with just MIMU sensors. These integrated sensors could extend stability and accuracy in the field of 3D analysis of human movement (3D AHM) if they can deliver position estimates with an accuracy close to 1 cm. Achieving this high accuracy of 1 cm remains challenging, with most studies reporting position estimation errors around 5 cm, often due to Non-Line of Sight (NLOS) conditions and systematic UWB sensor distance estimation errors. Studying the distance error characteristic of UWB in situations of 3D AHM is essential to deal with these errors. While research on UWB distance errors in Line of Sight (LOS) and NLOS situations exists, few studies focus on the NLOS errors caused by the human body and were not in the relevant scenarios of 3D AHM. Therefore, this article examines UWB sensor performance and distance error characteristics in LOS and NLOS situations typical for the 3D AHM. Both the LOS and NLOS situations were studied in the typical 3D AHM distance range of 0.2 m to 2 m. The NLOS situations were studied first with a human subject as NLOS causing object and then with simulated human body segments (PVC pipes filled with water) of varying diameters. In LOS situations, consistent systematic bias errors were observed, along with incidental errors at specific positions in the room. In NLOS scenarios caused by the human and simulated body segments, a consistent and reproducible overestimation of distances was found. The reproducibility of these errors based on relative node and object positions suggests that systematic mitigation methods could significantly reduce errors, enabling more accurate and reproducible 3D human movement analysis.

show abstract

NLOS Detection Generated by Body Shadowing in a 6.5 GHz UWB Localization System Using Machine Learning

et al. 2023

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

On Mitigation of Ranging Errors for Through-the-Body NLOS Conditions using Convolutional Neural Networks

Cited by 4 publications

References 4 publications

A comparative study on NLOS error elimination methods based on channel measurement experiment

A comparative study on NLOS error elimination methods based on channel measurement experiment

UWB distance estimation errors in (non-)line of sight situations within the context of 3D analysis of human movement

NLOS Detection Generated by Body Shadowing in a 6.5 GHz UWB Localization System Using Machine Learning

Contact Info

Product

Resources

About