GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors

Shang, Jing; Hu, Xuke; Wen, Cheng; Fan, Hongchao

doi:10.3390/s16122137

Cited by 11 publications

(8 citation statements)

References 21 publications

(27 reference statements)

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“…Wi-Fi [2,6,[12][13][14]17,25, RFID [19,20,93] IMU [5,9,15,17,20,[27][28][29][66][67][68][69][70][71][72][73]83,[93][94][95][96][97][98][99][100][101][102] BLU [9,10,18,74,75,82,86,89,92,95,96,[103][104][105][106][107]…”

Section: Type Of Technology Articlesmentioning

confidence: 99%

“…Many studies were motivated to enhance localization accuracy [3,8,71,73,95] for deadending handling issues [94] or to locate a single tagged object [19] or to achieve stable performance in the tracking process [64]. In order to improve accuracy, there must be a dataset that offers RSSI in a variety of settings and device configurations, along with the precision of location points [81], because it offers acceptable accuracy in office buildings with Wi-Fi access points [17].…”

Section: B Accuracymentioning

confidence: 99%

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Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

et al. 2023

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These recent years have witnessed the importance of indoor localization and tracking as people are spending more time indoors, which facilitates determining the location of an object. Indoor localization enables accurate and reliable location-based services and navigation within buildings, where GPS signals are often weak or unavailable. With the rapid progress of smartphones and their growing usage, smartphone-based positioning systems are applied in multiple applications. The smartphone is embedded with an inertial measurement unit (IMU) that consists of various sensors to determine the walking pattern of the user and form a pedestrian dead reckoning (PDR) algorithm for indoor navigation. As such, this study reviewed the literature on indoor localization based on smartphones. Articles published from 2015 to 2022 were retrieved from four databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. In total, 109 articles were reviewed from the 4186 identified based on inclusion and exclusion criteria. This study unveiled the technology and methods utilized to develop indoor localization systems. Analyses on sample size, walking patterns, phone poses, and sensor types reported in previous studies are disclosed in this study. Next, academic challenges, motivations, and recommendations for future research endeavors are discussed. Essentially, this systematic literature review (SLR) highlights the present research overview. The gaps identified from the SLR may assist future researchers in planning their research work to bridge those gaps.

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Section: Type Of Technology Articlesmentioning

confidence: 99%

Section: B Accuracymentioning

confidence: 99%

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

et al. 2023

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“…The indoor positioning technologies use wireless fidelity (Wi-Fi) [21]- [24], Bluetooth [25]- [28], vision [29]- [31], geomagnetism [32], inertial sensors-based localization [33], [34], ultrawide band [35], radio-frequency identification [36], ultrasound or sound [37], [38], light [39]- [42], and pedestrian dead reckoning (PDR) [43]. Among these technologies, Wi-Fi has the most advantages as compared to other infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Technologies Without Offline Fingerprinting Map: A Survey

Jang

Kim

2019

IEEE Commun. Surv. Tutorials

153

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Fingerprint-based wireless indoor positioning approaches are widely used for location-based services because wireless signals, such as Wi-Fi and Bluetooth, are currently pervasive in indoor spaces. The working principle of fingerprinting technology is to collect the fingerprints from an indoor environment, such as a room or a building, in advance, create a fingerprint map, and use this map to estimate the user's current location. The fingerprinting technology is associated with a high level of accuracy and reliability. However, the fingerprint map must be entirely re-created, not only when the Wi-Fi access points are added, modified, or removed, but also when the interior features, such as walls or even furniture, are changed, owing to the nature of the wireless signals. Many researchers have realized the problems in the fingerprinting technology and are conducting studies to address them. In this paper, we review the indoor positioning technologies that do not require the construction of offline fingerprint maps. We categorize them into simultaneous localization and mapping; inter/extrapolation; and crowdsourcing-based technologies, and describe their algorithms and characteristics, including advantages and disadvantages. We compare them in terms of our own parameters: accuracy, calculation time, versatility, robustness, security, and participation. Finally, we present the future research direction of the indoor positioning techniques. We believe that this paper provides valuable information on recent indoor localization technologies without offline fingerprinting map construction.

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“…To date, various indoor positioning technologies have been proposed to meet the increasing requirement of indoor ubiquitous location services [1], such as Pedestrian Dead Reckoning (PDR) [2][3][4], acoustic-based [5,6], visual-based [7,8], radio frequency-based [9,10], and magnetic field-based techniques [11,12]. Due to the popularity of Wi-Fi infrastructure and Wi-Fi-embedded mobile equipment, Wi-Fi-based positioning techniques have become increasingly popular.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

Luo

et al. 2020

IJGI

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Wi-Fi fingerprinting has been widely used for indoor localization because of its good cost-effectiveness. However, it suffers from relatively low localization accuracy and robustness owing to the signal fluctuations. Virtual Access Points (VAP) can effectively reduce the impact of signal fluctuation problem in Wi-Fi fingerprinting. Current techniques normally use the Log-Normal Shadowing Model to estimate the virtual location of the access point. This would lead to inaccurate location estimation due to the signal attenuation factor in the model, which is difficult to be determined. To overcome this challenge, in this study, we propose a novel approach to calculating the virtual location of the access points by using the Apollonius Circle theory, specifically the distance ratio, which can eliminate the attenuation parameter term in the original model. This is based on the assumption that neighboring locations share the same attenuation parameter corresponding to the signal attenuation caused by obstacles. We evaluated the proposed method in a laboratory building with three different kinds of scenes and 1194 test points in total. The experimental results show that the proposed approach can improve the accuracy and robustness of the Wi-Fi fingerprinting techniques and achieve state-of-art performance.

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GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors

Cited by 11 publications

References 21 publications

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

Indoor Positioning Technologies Without Offline Fingerprinting Map: A Survey

An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

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