Distance Estimation Algorithm for Wireless Localization Systems Based on Lyapunov Sensitivity Theory

Piccinni, G.; Torelli, Francesco; Avitabile, G.

doi:10.1109/access.2019.2947015

Cited by 4 publications

(2 citation statements)

References 36 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, the technologies related to indoor localization services are in high demand. There are many various wireless sensor networks (WSN)-based technologies that are used in the indoor localization field, such as Bluetooth, ZigBee, Wi-Fi, UWB and near-field communication (NFC) [1][2][3][4][5][6]. Most of them have indoor positioning network technology to determine the device's location and the target by utilizing algorithms with the received signal data.…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm Modelling for UWB Localization Accuracy in Remote Sensing

Chaczko²,

Shi

2022

Remote Sensing

View full text Add to dashboard Cite

At present, the ultra-wideband (UWB) technology plays a vital role in the environment of indoor localization. As a new technology of wireless communications, UWB has many advantages, such as high accuracy, strong anti-multipath ability, and high transmission rate. However, in real-time operation, the accuracy of UWB is reduced by multi-sensor interference, antenna variations and system operation noise. We have developed a novel error modelling based on the curve fitted Kalman filter (CFKF) algorithm to solve these issues. This paper involves investigating and developing the error modelling algorithm that can calibrate the signal sensors, reduce the errors, and mitigate noise levels and interference signals. As part of the research investigation, a range of experiments was executed to validate the CFKF error modelling approach’s accuracy, reliability and viability. The experimental results indicate that this novel approach significantly improves the accuracy and precision of beacon-based localization. Validation tests also show that the CFKF error modelling method can improve the localization accuracy of UWB-based solutions.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm Modelling for UWB Localization Accuracy in Remote Sensing

Chaczko²,

Shi

2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Anomaly detection is a common application of machine learning (ML) algorithms, mainly used for unsupervised learning problems and some supervised learning problems [17]. Different ML algorithms have been employed to address the NLoS ranging error by different researchers.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Based on Generalized Gaussian Distribution approach for Ultra-Wideband (UWB) Indoor Positioning System

Che¹,

Ahmed²,

Khan³

et al. 2021

Preprint

View full text Add to dashboard Cite

With the rapid development of the Internet of Things (IoT), Indoor Positioning System (IPS) has attracted significant interest in academic research. Ultra-Wideband (UWB) is an emerging technology that can be employed for IPS as it offers centimetre-level accuracy. However, the UWB system still faces several technical challenges in practice, one of which is Non-Line-of-Sight (NLoS) signal propagation.Several machine learning approaches have been applied for the NLoS component identification. However,

show abstract

Machine Learning Based Approach for Indoor Localization Using Ultra-Wide Bandwidth (UWB) System for Industrial Internet of Things (IIoT)

Che

Ahmed

Zaidi

et al. 2020

2020 International Conference on UK-China Emerging Technologies (UCET)

View full text Add to dashboard Cite

With the rapid development of wireless communication technology and the emergence of the Industrial Internet of Things (IIoT)s applications, high-precision Indoor Positioning Services (IPS) are urgently required. While the Global Positioning System (GPS) has been a key technology for outdoor localization, its limitation for indoor environments is well known. Ultra-WideBand (UWB) can help provide a very accurate position or localization for indoor harsh propagation environments. This paper focuses on improving the accuracy of the UWB indoor localization system including the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions by developing a Machine Learning (ML) algorithm. In this paper, a Naive Bayes (NB) ML algorithm is developed for UWB IPS. The performance of the developed algorithm is evaluated by Receiving Operating Curves (ROC)s. The results indicate that by employing the NB based ML algorithm significantly improves the localization accuracy of the UWB system for both the LoS and NLoS environment.

show abstract

Distance Estimation Algorithm for Wireless Localization Systems Based on Lyapunov Sensitivity Theory

Cited by 4 publications

References 36 publications

A Novel Algorithm Modelling for UWB Localization Accuracy in Remote Sensing

A Novel Algorithm Modelling for UWB Localization Accuracy in Remote Sensing

Anomaly Detection Based on Generalized Gaussian Distribution approach for Ultra-Wideband (UWB) Indoor Positioning System

Machine Learning Based Approach for Indoor Localization Using Ultra-Wide Bandwidth (UWB) System for Industrial Internet of Things (IIoT)

Contact Info

Product

Resources

About