Coursa Venue: Indoor Navigation Platform Using Fusion of Inertial Sensors with Magnetic and Radio Fingerprinting

Berkovich, Gennadii; Churikov, D. V.; Georgy, Jacques; Goodall, Chris

doi:10.23919/fusion43075.2019.9011323

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Ref. [30] proposed a uniform platform for the construction of a map and real time navigation, which is necessary for the entire process of the indoor positioning system based on fingerprinting. As the database for fingerprinting is constructed using the above schemes, methods for positioning within a short time are required.…”

Section: Related Papersmentioning

confidence: 99%

Position Estimation of Radio Source Based on Fingerprinting With Physical Wireless Parameter Conversion Sensor Networks

et al. 2023

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Establishing a highly accurate positioning of radio sources for radio wave monitoring and frequency spectrum sharing is attracting considerable attention. Because the positioning method generally requires specific processing of the position target, it is not applicable when the position target cannot perform any processing. The location fingerprinting method uses multiple sensors to observe the received signal strength indication (RSSI) emitted by a radio source and estimates the position from the radio wave propagation characteristics. This does not require a certain process for the target position. However, it takes time to gather RSSI from many sensors by wireless communication. In this study, we propose an RSSI gathering method using physical wireless parameter conversion sensor networks (PhyC-SN) for the highspeed positioning of radio sources. In the proposed method, each sensor selects the radio carrier frequency corresponding to its measured RSSI and transmits the signal. Projecting the RSSI distribution of each sensor onto the frequency distribution of the received signal enables the center to detect the RSSI of multiple sensors simultaneously. Furthermore, to improve the gathering accuracy, we established a sensor group method by considering the regional characteristics and access timing control based on each sensor group. Computer simulations and experimental evaluations show that the proposed method significantly reduces the data-gathering time compared with conventional packet communications and achieves a high positioning accuracy.

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Section: Related Papersmentioning

confidence: 99%

Position Estimation of Radio Source Based on Fingerprinting With Physical Wireless Parameter Conversion Sensor Networks

et al. 2023

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“…In the research works analysed, some authors used OGC standards to manage and represent indoor and outdoor maps [57]. Here we can observe the use of third-party maps which already full some standards, e.g., Google Maps [61,60,109,100,89,7,2], and also the use of custom maps [8,40,53,99,63,3,71,101] Position Technologies Section 2. is one of the standards used to evaluate indoor positioning system [114,32]. This standard consists of a set of guidelines for the standardisation of IPS/ILS which have been widely discussed within the indoor positioning community [115,116].…”

Section: What Improvements Were Done In Similar Studies (Rq5)mentioning

confidence: 98%

“…• Navigation [8,108,93,109,100,90,110,89,45,15]: considers whether proposed solutions oer navigation and routing services.…”

Section: What Improvements Were Done In Similar Studies (Rq5)mentioning

confidence: 99%

Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios

Quezada-Gaibor¹

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Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regression

Menzen,

Fetter,

Kok

2023

2023 26th International Conference on Information Fusion (FUSION)

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We present a mapping algorithm to compute largescale magnetic field maps in indoor environments with approximate Gaussian process (GP) regression. Mapping the spatial variations in the ambient magnetic field can be used for localization algorithms in indoor areas. To compute such a map, GP regression is a suitable tool because it provides predictions of the magnetic field at new locations along with uncertainty quantification. Because full GP regression has a complexity that grows cubically with the number of data points, approximations for GPs have been extensively studied. In this paper, we build on the structured kernel interpolation (SKI) framework, speeding up inference by exploiting efficient Krylov subspace methods. More specifically, we incorporate SKI with derivatives (D-SKI) into the scalar potential model for magnetic field modeling and compute both predictive mean and covariance with a complexity that is linear in the data points. In our simulations, we show that our method achieves better accuracy than current state-of-theart methods on magnetic field maps with a growing mapping area. In our large-scale experiments, we construct magnetic field maps from up to 40 000 three-dimensional magnetic field measurements in less than two minutes on a standard laptop.

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Coursa Venue: Indoor Navigation Platform Using Fusion of Inertial Sensors with Magnetic and Radio Fingerprinting

Cited by 4 publications

References 7 publications

Position Estimation of Radio Source Based on Fingerprinting With Physical Wireless Parameter Conversion Sensor Networks

Position Estimation of Radio Source Based on Fingerprinting With Physical Wireless Parameter Conversion Sensor Networks

Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios

Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regression

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