Precise 3-D Indoor Localization Based on Wi-Fi FTM and Built-In Sensors

Yu, Yue; Chen, Ruizhi; Chen, Liang; Xu, Shihao; Li, Wei; Wu, Yuan; Zhou, Haitao

doi:10.1109/jiot.2020.2999626

Cited by 72 publications

(33 citation statements)

References 47 publications

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“…Where TOF i is the time of flight of the signal from BSi/WAPi to the target device [52]; ∆ is the signals processing delay time beside receiver and transmitters as presented in Fig. 10. = .…”

Section: Fig 10 Rtt Localization Techniquementioning

confidence: 99%

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

Asaad¹,

Maghdid²

2021

Preprint

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<p>The number of connected mobile devices and Internet of Things (IoT) is growing around us, rapidly. Since, most of the people daily activities are relying on these connected things or devices. Specifically, this past year (with COVID-19) changed daily life in abroad and this is increased the use of IoT enabled technologies in health sector, work, and play. Further, the most common service via using these technologies is the localization/positioning service for different applications including: geo-tagging, billing, contact tracing, health-care system, point-of-interest recommendations, social networking, security, and more. Despite the availability of a large number of localization solutions in the literature, the precision of localization cannot meet the needs of consumers. For that reason, this paper provides an in-depth investigation of the existing technologies and techniques in the localization field, within the IoT era. Furthermore, the benefits and drawbacks of each technique with enabled technologies are illustrated and a comparison between the utilized technologies in the localization is made. The paper as a guideline is also going through all of the metrics that may be used to assess the localization solutions. Finally, the state-of-the-art solutions are examined, with challenges and perspectives regarding indoors/outdoors environments are demonstrated.</p>

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Section: Fig 10 Rtt Localization Techniquementioning

confidence: 99%

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

Asaad¹,

Maghdid²

2021

Preprint

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“…Yu et al [14] proposed an accurate 3D-WFBS method. The AEKF is utilized for estimating the pedestrian real world walking and heading speed, and the obtained round trip time and signal strength indication gathered from WiFi APs are integrated to detect proximity and provide precise ranging results.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of deer hunting optimization algorithm for accurate 3D indoor node localization

Durgaprasadarao

Siddaiah

2021

Evol. Intel.

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Indoor localization is one of the emergent technologies in location based services, which find useful for commercial as well as civilian industries. Global position systems are a familiar solution for outdoor localization systems. But the presence of complicated obstacles in buildings poses a major challenge in indoor localization. Though few indoor localization techniques based on ranging and fingerprint based techniques are devised, they are time consuming and laborious. Therefore, this paper devises an efficient deer hunting optimization algorithm with weighted least square estimation (DHOA-WLSE) technique for accurate 3D indoor node localization technique. The proposed DHOA-WLSE technique has the ability to accomplish minimal localization error with least localization time. In DHOA-WLSE technique, the DHOA is used to estimate the primary target location to eliminate the non-line of sight errors. Based on the primary locations attained, the WLSE technique is applied to determine the accurate target's final location. In order to validate the 3D indoor localization performance of the DHOA-WLSE technique, an extensive simulation analysis is performed and the results are investigated in terms of different measures. The simulation outcomes demonstrated the superior performance of the DHOA-WLSE technique over the recent state of art techniques.

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“…A similar approach is made in [19], where the positions estimated by PDR and the distances converted from WiFi RTTs are used as inputs of EKF. In [20], the PDR's measurements are precalibrated by EKF. The result is then fused with the estimated distances from WiFi RTTs to enable 3D positioning by an unscented particle filter, another state estimation filter.…”

Section: Integrating Wifi Positioning and Pdrmentioning

confidence: 99%

Exploiting User Mobility for WiFi RTT Positioning: A Geometric Approach

Han

Kim

et al. 2020

Preprint

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Due to the massive deployment of WiFi APs and its accessibility to various positioning elements, WiFi positioning is a key enabler to provide seamless and ubiquitous location services to users. There are various kinds of WiFi positioning technologies, depending on the concerned positioning element. Among them, round-trip time (RTT) measured by a fine-timing measurement protocol has received great attention recently. It provides an acceptable ranging accuracy near the service requirements in favorable environments when a line-of-sight (LOS) path exists. Otherwise, a signal is detoured along with non-LOS paths, making the resultant ranging results different from the groundtruth. The difference between the two is called an RTT bias, which is the main reason for poor positioning performance. To address it, we aim at leveraging the history of user mobility detected by a smartphone's inertial measurement units, called pedestrian dead reckoning (PDR). Specifically, PDR provides the geographic relation among adjacent locations, guiding the resultant positioning estimates' sequence not to deviate from the user trajectory. To this end, we describe their relations as multiple geometric equations, enabling us to render a novel positioning algorithm with acceptable accuracy. The algorithm is designed into two phases. First, an RTT bias of each AP can be compensated by leveraging the geometric relation mentioned above. It provides a user's relative trajectory defined on the local coordinate system of the concerned AP. Second, the user's absolute trajectory can be found by rotating every relative trajectory to be aligned, called trajectory alignment. The proposed algorithm gives a unique position when the number of detected steps and APs is at least 4 and 3 for linear mobility and 5 and 2 for arbitrary mobility. Various field experiments extensively verify the proposed algorithm's effectiveness that the average positioning error is approximately 0.369 (m) and 1.705 (m) in LOS and NLOS environments, respectively.

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Precise 3-D Indoor Localization Based on Wi-Fi FTM and Built-In Sensors

Cited by 72 publications

References 47 publications

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

Design of deer hunting optimization algorithm for accurate 3D indoor node localization

Exploiting User Mobility for WiFi RTT Positioning: A Geometric Approach

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