Use of Artificial Magnetic Anomalies in Indoor Pedestrian Navigation

Kemppi, Paul; Pajunen, J.; Rautiainen, T.

doi:10.1109/vetecf.2010.5594106

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…An artificial magnetic field is produced using the current coils with a specific number of turns of wire wrapped on a core. Sheinker et al, Blankenbach and Norrdine, and Kemppi et al use the same concept and achieve an accuracy of 1 m for indoor localization. These systems accomplish high accuracy, as the active magnetic fields are strong and less affected by the environment.…”

Section: Related Workmentioning

confidence: 99%

GUIDE: Smartphone sensors‐based pedestrian indoor localization with heterogeneous devices

Ashraf

Hur

Shafiq

et al. 2019

Int J Communication

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Summary A plethora of indoor localization systems based on Wi‐Fi, radio frequency chips, ultra‐wide‐band, and bluetooth have been proposed, yet these systems do not work when the infrastructure is absent. On the other hand, infrastructure less systems benefit mostly from off‐the‐shelf smartphone sensors and do not need additional hardware. This study shows a similar indoor localization approach which turns smartphone built‐in sensors to good account. We take advantage of magnetic field strength fingerprinting approach to localize a pedestrian indoor. In addition, accelerometer and gyroscope sensors are utilized to find the pedestrian's traveled distance and heading estimation, respectively. Our aim is to solve the problem of device dependence by devising an approach that can perform localization using various smartphones in a similar fashion. We make the use of patterns of magnetic field strength to formulate the fingerprint database to achieve this goal. This approach solves two problems: need to update the database periodically and device dependence. We conduct experiments using Samsung Galaxy S8 and LG G6 for five different buildings with different dimensions in Yeungnam University, Republic of Korea. The evaluation is performed by following three different path geometries inside the buildings. The results show that the proposed localization approach can potentially be used for indoor localization with heterogeneous devices. The errors for path 1 and path 2 are very similar, however, localization error for path 3 is comparatively higher because of the complexity of the path 3. The mean and median errors for Galaxy S8 are 1.37 and 0.88 m while for LG G6, these are 1.84 and 1.21 m, respectively, while considering all buildings and all paths followed during the experiment. Overall, the proposed approach can potentially localize a pedestrian within 1.21 m at 50% and within 1.93 m at 75%, irrespective of the device used for localization. The performance of the proposed approach is compared with the K nearest neighbor (KNN) for evaluation. The proposed approach outperforms the KNN

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Section: Related Workmentioning

confidence: 99%

GUIDE: Smartphone sensors‐based pedestrian indoor localization with heterogeneous devices

Ashraf

Hur

Shafiq

et al. 2019

Int J Communication

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show abstract

“…A position detection technique using magnetic sensors has also been developed. This is a method for detecting the position based on the deformed magnetic field anomaly measured in the building and comparing it with the measured value [19,20,21,22,23,24,25]. Indoor position detection methods using magnetic sensors have the advantages that the absolute position is measured without any additional information and that no infrastructure is necessary compared with WLAN-based positioning methods.…”

Section: Introductionmentioning

confidence: 99%

Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness Weighting

Kim

Choi

Kim

et al. 2019

Sensors

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This research proposes an algorithm that improves the position accuracy of indoor pedestrian dead reckoning, by compensating the position error with a magnetic field map-matching technique, using multiple magnetic sensors and an outlier mitigation technique based on roughness weighting factors. Since pedestrian dead reckoning using a zero velocity update (ZUPT) does not use position measurements but zero velocity measurements in a stance phase, the position error cannot be compensated, which results in the divergence of the position error. Therefore, more accurate pedestrian dead reckoning is achievable when the position measurements are used for position error compensation. Unfortunately, the position information cannot be easily obtained for indoor navigation, unlike in outdoor navigation cases. In this paper, we propose a method to determine the position based on the magnetic field map matching by using the importance sampling method and multiple magnetic sensors. The proposed method does not simply integrate multiple sensors but uses the normalization and roughness weighting method for outlier mitigation. To implement the indoor pedestrian navigation algorithm more accurately than in existing indoor pedestrian navigation, a 15th-order error model and an importance-sampling extended Kalman filter was utilized to correct the error of the map-matching-aided pedestrian dead reckoning (MAPDR). To verify the performance of the proposed indoor MAPDR algorithm, many experiments were conducted and compared with conventional pedestrian dead reckoning. The experimental results show that the proposed magnetic field MAPDR algorithm provides clear performance improvement in all indoor environments.

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“…Kemppi et al, investigated the use of pre‐stationed magnetic field emitters in doorways of a building to determine position for pedestrian navigation . Each emitter is unique and identifies a particular location in the building, as well as having greater strength than Earth's magnetic field or magnetic field perturbations due to the environment.…”

Section: Introductionmentioning

confidence: 99%

Navigation of Ground Vehicles Using Magnetic Field Variations

Shockley

Raquet

2014

J Inst Navig

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Inexpensive three‐axis magnetometers can sense not just heading, but also variations in Earth's magnetic field. Combined with a magnetic field map, this information can be used for positioning. Pairing the three‐axis magnetometer measurements with position information from a ground vehicle creates a magnetic field map containing the variation and localized perturbations in Earth's magnetic field as a function of position. A magnetometer and the magnetic field map can then be used for navigation. This paper describes how magnetometer measurements can be used to calculate a position update using three different likelihood functions, and it also demonstrates direct incorporation of magnetic field measurements into a map‐matching particle filter. These algorithms were tested in three different vehicles under a variety of road conditions and demonstrated meter‐level positioning when local magnetic field features are available. Overall the information is sufficient for road level navigation when used as the sole source of navigation information. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

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Use of Artificial Magnetic Anomalies in Indoor Pedestrian Navigation

Cited by 10 publications

References 7 publications

GUIDE: Smartphone sensors‐based pedestrian indoor localization with heterogeneous devices

GUIDE: Smartphone sensors‐based pedestrian indoor localization with heterogeneous devices

Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness Weighting

Navigation of Ground Vehicles Using Magnetic Field Variations

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