XJTLUIndoorLoc: A New Fingerprinting Database for Indoor Localization and Trajectory Estimation Based on Wi-Fi RSS and Geomagnetic Field

Zhong, Zhenghang; Tang, Zhe; Li, Xiangxing; Yuan, Tiancheng; Yang, Yang; Meng, Wei; Zhang, Yuanyuan; Sheng, Renzhi; Grant, Naomi; Ling, Chongfeng; Huan, Xintao; Kim, Kyeong Soo; Lee, Sanghyuk

doi:10.1109/candarw.2018.00050

Cited by 19 publications

(8 citation statements)

References 13 publications

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“…Other WiFibased multi-floor data sets include UTSIndoorLoc [70] which is used in systems such as [81], [88], and JUIndoorLoc [21]. Moreover, MagWi [22] and XJTLUIndoorLoc [23] are examples of data sets covering both magnetism and WiFi in multi-floor environments. More data sets are needed to cover different technologies in multi-floor environments such as BLE and Cellular.…”

Section: B Experimentation In Localization Systemsmentioning

confidence: 99%

A Survey of Indoor Localization Systems in Multi-Floor Environments

Mostafa¹,

Harras²,

Youssef³

2022

Preprint

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This work presents a survey for the indoor localization systems handling multi-floor environments, including floor localization, and estimating the floor and the 2D location of the user. It focuses on approaches targetting users and user devices. The survey comprehensively covers various techniques and technologies focusing especially on the recent solutions to give insights to the researchers for further development in the domain of indoor localization. Finally, several challenges are addressed and possible solutions are proposed pointing to future research directions. No data was collected for this work.

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Section: B Experimentation In Localization Systemsmentioning

confidence: 99%

A Survey of Indoor Localization Systems in Multi-Floor Environments

Mostafa¹,

Harras²,

Youssef³

2022

Preprint

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“…A fingerprint dataset to perform indoor localization and trajectory estimation is presented in [55]. The dataset is collected in a multi-story building of Xi'an Jiatong-Liverpool University, Suzhou, China.…”

Section: Xjtluindoorlocmentioning

confidence: 99%

“…The space structure used for the data collection in [55] is small and the path geometry simple. Despite the data collection from various directions at each location point, a single orientation is used.…”

Section: Xjtluindoorlocmentioning

confidence: 99%

Empirical Overview of Benchmark Datasets for Geomagnetic Field-Based Indoor Positioning

Ashraf

Din

Hur

et al. 2021

Sensors

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Indoor positioning and localization have been regarded as some of the most widely researched areas during the last decade. The wide proliferation of smartphones and the availability of fast-speed internet have initiated several location-based services. Concerning the importance of precise location information, many sensors are embedded into modern smartphones. Besides Wi-Fi positioning, a rich variety of technologies have been introduced or adopted for indoor positioning such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, etc. However, special emphasis is put on infrastructureless approaches like Wi-Fi and magnetic field-based positioning, as they do not require additional infrastructure. Magnetic field positioning is an attractive solution for indoors; yet lack of public benchmarks and selection of suitable benchmarks are among the big challenges. While several benchmarks have been introduced over time, the selection criteria of a benchmark are not properly defined, which leads to positioning results that lack generalization. This study aims at analyzing various public benchmarks for magnetic field positioning and highlights their pros and cons for evaluation positioning algorithms. The concept of DUST (device, user, space, time) and DOWTS (dynamicity, orientation, walk, trajectory, and sensor fusion) is introduced which divides the characteristics of the magnetic field dataset into basic and advanced groups and discusses the publicly available datasets accordingly.

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“…The LSTM result shows that it reduces the vanishing gradient problem [33] which exists when we use RNN networks for UWB localization. In this paper we followed the LSTM model presented in References [34,35] for deep LSTM implementation. The LSTM model consists of a layered structure with a state c (cell state) in each hidden layer.…”

Section: Lstm Based Uwb Localizationmentioning

confidence: 99%

UWB Indoor Localization Using Deep Learning LSTM Networks

2020

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Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user position. The proposed LSTM model receives the distance values from TOA-distance model of the UWB system and predicts the current user position. The performance of the proposed LSTM model-based UWB localization system is analyzed in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes, timesteps, and we also compared the mean localization accuracy of the system with different deep learning models and conventional UWB localization approaches. The simulation results show that the proposed UWB localization approach achieved a 7 cm mean localization error as compared to conventional UWB localization approaches.

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XJTLUIndoorLoc: A New Fingerprinting Database for Indoor Localization and Trajectory Estimation Based on Wi-Fi RSS and Geomagnetic Field

Cited by 19 publications

References 13 publications

A Survey of Indoor Localization Systems in Multi-Floor Environments

A Survey of Indoor Localization Systems in Multi-Floor Environments

Empirical Overview of Benchmark Datasets for Geomagnetic Field-Based Indoor Positioning

UWB Indoor Localization Using Deep Learning LSTM Networks

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