A Novel Algorithm Modelling for UWB Localization Accuracy in Remote Sensing

Yu, Zhengyu; Chaczko, Zenon; Shi, Jiajia

doi:10.3390/rs14194902

Cited by 3 publications

(2 citation statements)

References 32 publications

(36 reference statements)

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“…Furthermore, with recent advancements in Bluetooth technology, many systems leverage BLE beacons for indoor localization [21], [22]. Another promising communication technology for IPS is UWB [23], which offers advantages such as low power consumption, high data rates (up to 1 Gb), and remarkable accuracy. The results of the survey conducted in [24] VOLUME 12, 2024 Although less prevalent than Wi-Fi and Bluetooth, ZigBee has attracted attention for localization applications owing to its low power consumption and widespread use in IoT deployments.…”

Section: A General Rf Indoor Localization Technologiesmentioning

confidence: 99%

Augmentation of Fingerprints for Indoor BLE Localization Using Conditional GANs

Junoh,

Pyun

2024

IEEE Access

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Location estimation in indoor environments using radiofrequency (RF) has garnered considerable attention in recent years owing to the widespread adoption of mobile devices. RF-based fingerprinting-a direct approach that allows location estimation based on observed signals-relies on manual surveys during the offline phase to create a radio map with coordinates and RF measurements at multiple locations. The accuracy of RF fingerprint-based localization is proportional to the number of reference points. However, conventional site survey procedures incur substantial expenses. To alleviate the workload of site surveys and address the challenge of incomplete fingerprint databases, we propose a data-augmentation method to complement existing fingerprint data. Our approach leverages a conditional generative adversarial network with long short-term memory (CGAN-LSTM) prediction model to effectively learn the intricate patterns inherent in the initial training data and generate high-quality synthetic data that align with the underlying data distribution. In an experimental evaluation conducted on a real testbed, our data augmentation framework increased the average localization accuracy by 15.74% compared with fingerprinting without data augmentation. Compared with linear interpolation, inverse distance weighting, and Gaussian process regression, the proposed approach demonstrates an average accuracy improvement ranging from 1.84% to 14.04%, achieving average accuracies of 1.065 and 1.956 m in both scenarios. In experiments conducted in two typical indoor environments using sparse data, the proposed approach substantially reduced localization error and proved comparable to state-of-the-art data-augmentation methods. INDEX TERMSBluetooth low energy (BLE), fingerprint, data augmentation, generative adversarial network (GAN), location estimation. SUHARDI AZLIY JUNOH (Graduate Student Member, IEEE) received the B.Eng. degree in electronics (telecommunications) from Multimedia University, Malaysia, and the master's degree in electrical engineering from Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia. He is currently pursuing the Ph.D. degree in information and communication engineering with Chosun University, South Korea. He was with Infineon Technologies as an Engineer. His current research interests include indoor positioning and navigation, the Internet of Things, mobile computing, and wireless communication systems. JAE-YOUNG PYUN (Member, IEEE) received the B.S. and M.

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Section: A General Rf Indoor Localization Technologiesmentioning

confidence: 99%

Augmentation of Fingerprints for Indoor BLE Localization Using Conditional GANs

Junoh,

Pyun

2024

IEEE Access

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“…At present, many positioning methods complementary to GNSS have been developed to provide ubiquitous location-based services (LBS). Compared to positioning methods that require the deployment of positioning facilities in advance, such as WiFi [2], Ultra-Wide Band (UWB) [3], Long Term Evolution (LTE) [4], 5G [5], audio [6], etc. Pedestrian Dead Reckoning (PDR) is an independent positioning method.…”

Section: Introductionmentioning

confidence: 99%

Robust Pedestrian Dead Reckoning Integrating Magnetic Field Signals and Digital Terrestrial Multimedia Broadcasting Signals

et al. 2023

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Currently, many positioning technologies complementary to Global Navigation Satellite System (GNSS) are providing ubiquitous positioning services, especially the coupling positioning of Pedestrian Dead Reckoning (PDR) and other signals. Magnetic field signals are stable and ubiquitous, while Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong penetration and stable transmission over a large range. To improve the positioning performance of PDR, this paper proposes a robust PDR integrating magnetic field signals and DTMB signals. In our study, the Spiking Neural Network (SNN) is first used to learn the magnetic field signals of the environment, and then the learning model is used to detect the magnetic field landmarks. At the same time, the DTMB signals are collected by the self-developed signal receiver, and then the carrier phase ranging of the DTMB signals is realized. Finally, robust pedestrian positioning is achieved by integrating position information from magnetic field landmarks and ranging information from DTMB signals through Extended Kalman Filter (EKF). We have conducted indoor and outdoor field tests to verify the proposed method, and the outdoor field test results showed that the positioning error cumulative distribution of the proposed method reaches 2.84 m at a 68% probability level, while that of the PDR only reaches 8.77 m. The proposed method has been validated to be effective and has good positioning performance, providing an alternative solution for seamless indoor and outdoor positioning.

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Indoor Localization With Distributed 5G Small Cells Considering Time Alignment Errors

Wang,

Xu,

et al. 2024

IEEE Sensors J.

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A Novel Algorithm Modelling for UWB Localization Accuracy in Remote Sensing

Cited by 3 publications

References 32 publications

Augmentation of Fingerprints for Indoor BLE Localization Using Conditional GANs

Augmentation of Fingerprints for Indoor BLE Localization Using Conditional GANs

Robust Pedestrian Dead Reckoning Integrating Magnetic Field Signals and Digital Terrestrial Multimedia Broadcasting Signals

Indoor Localization With Distributed 5G Small Cells Considering Time Alignment Errors

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