The analysis of magnetic disturbance due to buildings

Yamazaki, K.; Kato, K.; Ono, K.; Saegusa, H.; Iida, Y.; Yamamoto, S.; Tokunaga, K.; Ashiho, K.; Fujiwara, K.; Takahashi, N.

doi:10.1109/intmag.2003.1230378

Cited by 4 publications

(4 citation statements)

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“…fingerprint. These distortions in magnetic field come from ferromagnetic fluctuations caused by the building's metal construction and general topology [55,173]. MEMS magnetometers [23,143] are the most commonly used sensors in service of indoor localisation, due to their relatively low cost and high sensitivity [59].…”

Section: Magnetometer Sensorsmentioning

confidence: 99%

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Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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Section: Magnetometer Sensorsmentioning

confidence: 99%

“…Magnetic field sensing has been proven to be effective, but only in confined spaces, taking advantage of ferromagnetic effects brought on by buildings [173], and under controlled conditions [52,55]. This type of localisation also suffers from fingerprinting issues [23,158].…”

Section: Drawbacks and Modality Evaluationmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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“…Static and extremely low-frequency (ELF) magnetic fields in modern buildings arise from both natural and man-made sources, such as electric power systems, electric and electronic appliances, and industrial devices. Steel and reinforced concrete structures of buildings cause fluctuations in the ambient magnetic field [22]. Static and ELF fields are mainly considered to be harmful, as they can cause electromagnetic interferences to sensitive electrical devices such as video display units and sophisticated measurement instruments.…”

Section: Global Self-localizationmentioning

confidence: 99%

A wireless sensor system for global self-localization and collaborative actioning

Haverinen

Martinez

Pirttikangas

2009

2009 Sixth International Conference on Networked Sensing Systems (INSS)

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Recent progress in MEMS technology and miniaturization together with low power radio solutions have enabled new concepts of wireless sensor devices and applications. In this article, we present a wireless sensor system developed for conducting experiments in global self-localization, collaborative actioning, and gestural control. The sensor system consists of multiple wireless nodes, based on the nRF24L01 radio transceiver, and a base station. Each sensor node includes 3-axis accelerometer, atmospheric pressure sensor, photo detector, and a microphone. For global self-localization experiments 3-axis magnetometer was added to a sensor node by using the expansion bus of the node. Depending on the application the sensor data is stored into the flash memory inside the base station or sent directly to a host computer through the radio link. The selected applications and preliminary results are also presented.

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“…Reinforced concrete structure interferes with the indoor magnetic environment, which makes the magnetic information become rich. It is the rich magnetic information that makes indoor localization based on magnetic field come true [16] [17].…”

Section: Introductionmentioning

confidence: 99%

Robust Local Weighted Regression for Magnetic Map-Based Localization on Smartphone Platform

Meng¹,

Wang²,

Wang³

et al. 2017

JCC

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The magnetic information measured on the smartphone platform has a large fluctuation and the research of indoor localization algorithm based on smartphone platform is less. Indoor localization algorithm on smartphone platform based on particle filter is studied. Robust local weighted regression is used to smooth the original magnetic data in the process of constructing magnetic map. Use moving average filtering model to filter the online magnetic observation data in positioning process. Compare processed online magnetic data with processed magnetic map collected by smartphone platform and the average matching error is 0.3941uT. Average positioning error is 0.229 meter when using processed online and map data.

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The analysis of magnetic disturbance due to buildings

Cited by 4 publications

References 1 publication

Sensor Modalities and Fusion for Robust Indoor Localisation

Sensor Modalities and Fusion for Robust Indoor Localisation

A wireless sensor system for global self-localization and collaborative actioning

Robust Local Weighted Regression for Magnetic Map-Based Localization on Smartphone Platform

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