Indoor location sensing using geo-magnetism

Chung, Jaewoo; Donahoe, Matt; Schmandt, Chris; Kim, Ig-Jae; Razavai, Pedram; Wiseman, Micaela

doi:10.1145/1999995.2000010

Cited by 374 publications

(239 citation statements)

References 16 publications

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“…Geomagnetic positioning in indoor environments and buildings is mainly influenced by the steel skeleton structure. Li et al [82] deeply discussed the feasibility of geomagnetic location, and thought that there were still many problems to be solved despite the obvious advantages of geomagnetic location.…”

Section: Geomagnetic-based Indoor Positioning Technologymentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

195

125

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Abstract:The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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Section: Geomagnetic-based Indoor Positioning Technologymentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

195

125

View full text Add to dashboard Cite

show abstract

“…On the other hand, Chung et al (2011) and Montoliu et al (2016) made use of disturbances of the Earth's magnetic field and that of structural steel elements in a building to design an indoor positioning system. Their study entails investigating the characteristics of magnetic field fingerprints, the performance of the positioning system using the fingerprints and its implementation in a pedestrian navigation system.…”

Section: Magnetic Positioning Systemmentioning

confidence: 99%

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Sakpere

Oshin

Mlitwa

2017

SACJ

147

129

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The research and use of positioning and navigation technologies outdoors has seen a steady and exponential growth. Based on this success, there have been attempts to implement these technologies indoors, leading to numerous studies. Most of the algorithms, techniques and technologies used have been implemented outdoors. However, how they fare indoors is different altogether. Thus, several technologies have been proposed and implemented to improve positioning and navigation indoors. Among them are Infrared (IR), Ultrasound, Audible Sound, Magnetic, Optical and Vision, Radio Frequency (RF), Visible Light, Pedestrian Dead Reckoning (PDR)/Inertial Navigation System (INS) and Hybrid. The RF technologies include Bluetooth, Ultra-wideband (UWB), Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN), Radio-Frequency Identification (RFID) and Near Field Communication (NFC). In addition, positioning techniques applied in indoor positioning systems include the signal properties and positioning algorithms. The prevalent signal properties are Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Received Signal Strength Indication (RSSI), while the positioning algorithms are Triangulation, Trilateration, Proximity and Scene Analysis/ Fingerprinting. This paper presents a state-of-the-art survey of indoor positioning and navigation systems and technologies, and their use in various scenarios. It analyses distinct positioning technology metrics such as accuracy, complexity, cost, privacy, scalability and usability. This paper has profound implications for future studies of positioning and navigation.

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“…Long-term effect of ocean model estimation inaccuracy, however, can become significant, which can be observed from the comparison between the navigation performance for the first 6 hours (8% UDT) and the entire 24 hours (25% UDT). Secondly, information fusion between INS-based inference and current-aiding feedback is handled through the EKFs, which weigh the state estimator's "confidence" between dead-reckoning and the ocean model by proper selection of values for Q in (14) and R in (16). This prevents the state estimator from becoming overconfident in ocean model estimate before the dead-reckoning uncertainty increases to the level of ocean model inaccuracy.…”

Section: Evaluation With Field Test Datamentioning

confidence: 99%

“…In contrast, methods based on field features utilize a certain type of background field, either natural or artificial, as the navigation reference. The robot is able to perform correction for positioning error at any location by measuring the local strength of the background field [16], [17]. This type of methods significantly expands the robot's navigation range with a prerequisite that a map of the field needs to be known beforehand.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

Song

Mohseni

2018

IEEE J. Oceanic Eng.

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A current-aided inertial navigation framework is proposed for small autonomous underwater vehicles in longduration operations (> 1 hour), where neither frequent surfacing nor consistent bottom-tracking are available. We instantiate this concept through mid-depth, underwater navigation. This strategy mitigates dead-reckoning uncertainty of a traditional inertial navigation system by comparing the estimate of local, ambient flow velocity with preloaded ocean current maps. The proposed navigation system is implemented through a marginalized particle filter where the vehicle's states are sequentially tracked along with sensor bias and local turbulence that is not resolved by general flow prediction. The performance of the proposed approach is first analyzed through Monte Carlo simulations in two artificial background flow fields, resembling real-world ocean circulation patterns, superposed with smaller-scale, turbulent components with Kolmogorov energy spectrum. The current-aided navigation scheme significantly improves the dead-reckoning performance of the vehicle even when unresolved, small-scale flow perturbations are present. For a 6-hour navigation with an automotive-grade inertial navigation system, the currentaided navigation scheme results in positioning estimates with under 3% uncertainty per distance traveled (UDT) in a turbulent, double-gyre flow field, and under 7.3% UDT in a turbulent, meandering jet flow field. Further evaluation with field test data and actual ocean simulation analysis demonstrates consistent performance for a 6-hour mission, positioning result with under 25% UDT for a 24-hour navigation when provided direct heading measurements, and terminal positioning estimate with 16% UDT at the cost of increased uncertainty at an early stage of the navigation.

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Indoor location sensing using geo-magnetism

Cited by 374 publications

References 16 publications

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

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