Convolutional Neural Network-based UWB System Localization

Nguyen, Doan Tan Anh; Lee, Han-Gyeol; Joung, Jingon; Jeong, Eui-Rim

doi:10.1109/ictc49870.2020.9289326

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…However, this study only took TOA for comparison without considering other deep learning models. In the literature 33 , it was proposed to transmit UWB signals to three different receiving antenna devices and convert the signals into RGB images. By using a CNN model to process the received image and estimate the location of the label, this method does not require predicting the distance between the transmitter and the receiver.…”

Section: Uwb Positioning Based On Deep Learningmentioning

confidence: 99%

Application of a long short-term memory neural network algorithm fused with Kalman filter in UWB indoor positioning

Tian,

Lian,

Wang

et al. 2024

Sci Rep

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Ultra-wideband technology has good anti-interference capabilities and development prospects in indoor positioning. Since ultra-wideband will be affected by random errors in indoor positioning, to exploit the advantages of the Kalman filter (KF) and the long short-term memory (LSTM) network, this paper proposes a long short-term memory neural network algorithm fused with the Kalman filter (KF–LSTM) to improve UWB positioning. First, the ultra-wideband data is processed through KF to weaken the noise in the data, and then the data is fed into the LSTM network for training, and the capability of the LSTM network to process time series features is employed to obtain more accurate label positions. Finally, simulation and measurement results show that the KF–LSTM algorithm achieves 71.31%, 37.28%, and 49.31% higher average positioning accuracy than the back propagation (BP) network, (back propagation network fused with the Kalman filter (KF-BP), and LSTM network algorithms, respectively, and the KF–LSTM algorithm performs more stably. Meanwhile, the more noise the data contains, the more obvious the stability contrast between the four algorithms.

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Section: Uwb Positioning Based On Deep Learningmentioning

confidence: 99%

Application of a long short-term memory neural network algorithm fused with Kalman filter in UWB indoor positioning

Tian,

Lian,

Wang

et al. 2024

Sci Rep

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“…In order to minimize the loss of signals from the base stations, a tightly-coupled algorithm that combines the measurements of MIMU (Miniature Inertial Measurement Unit) and the UWB signals [16] was proposed. In addition, some researches have applied neural networks to indoor localization algorithms, using the localization method of Convolutional Neural Network (CNN) networks [17] to generate the corresponding RGB image information from the received UWB signals [18] and directly estimate the location of the tag without a distancemeasuring algorithm.…”

Section: 4uwb Fusion Positioning Algorithmmentioning

confidence: 99%

A review of UWB positioning technology applications in personnel security management

Zhao

Zhu

et al. 2023

Third International Conference on Digital Signal and Computer Communications (DSCC 2023)

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With the rise of Internet of Things (IoT) technology, the technical requirements for indoor positioning technology are gradually growing. Indoor high-precision positioning technology is obtaining widespread attention from industrial areas, especially the field of safety management for hazardous operators. Ultra-wideband (UWB) technology has become one of research focuses for indoor positioning technology research because of its outperformances treating with multipath resistance, interference resistance, owning high penetration and high accuracy. With the improvements of the management standards, enterprises increasingly need precise positioning in personnel security management, movement track and activity area restrictions. To outline the current development status of the state-of-art in positioning technologies, this study introduces the development and algorithms of UWB positioning technology and related applications in the fields of personnel security, providing a comprehensive reference for personnel positioning and security monitoring. Finally, we introduce an effective positioning approach based on a hand-ring UWB tag, which is capable of real-time monitoring of life safety indicators, such as heart rate and blood oxygen. This hand-ring UWB tag can monitor the both position and human-body healthy status, and will be widely used in various fields in the future.

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Improving Indoor Localization Using Mobile UWB Sensor and Deep Reinforcement Learning

Nosrati,

Semnani,

Fazel

et al. 2024

IEEE Sensors J.

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Convolutional Neural Network-based UWB System Localization

Cited by 4 publications

References 5 publications

Application of a long short-term memory neural network algorithm fused with Kalman filter in UWB indoor positioning

Application of a long short-term memory neural network algorithm fused with Kalman filter in UWB indoor positioning

A review of UWB positioning technology applications in personnel security management

Improving Indoor Localization Using Mobile UWB Sensor and Deep Reinforcement Learning

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