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A spatio-temporal database for positioning systems: the case of CellLocate
Marco Tosolini, 2014, master's thesis

Found in: osebi
Keywords: database, positioning systems, cellular network
Published: 25.01.2017; Views: 2711; Downloads: 0
.pdf Fulltext (1,00 MB)

Dealing with network changes in cellular fingerprint positioning systems
Chris Marshall, Federico Pittino, Andrea Dalla Torre, Donatella Gubiani, Angelo Montanari, Paolo Gallo, Andrea Viel, 2017, published scientific conference contribution

Abstract: Besides being a fundamental infrastructure for communication, cellular networks are exploited for positioning through signal fingerprinting. Maintaining the fingerprint database consistent and up-to-date is a challenging task in many fingerprint positioning systems, e.g., in those populated by a crowd-sourcing effort. To this end, detecting and tracking the changes in the configurations of cellular networks over time is recognized as a relevant problem. In this paper, we show that to cope with this problem we can successfully exploit information provided by Timing Advance (TA). As a by-product, we prove that TA can improve the fingerprint candidate selection phase, reducing the number of fingerprints to provide as input to positioning algorithms. The effectiveness of the proposed improvements has been tested on a fingerprint positioning system with a large fingerprint dataset collected over a period of 2 years.
Found in: osebi
Keywords: fingerprint positioning systems, cellular communication networks, network changes
Published: 13.06.2018; Views: 2323; Downloads: 0
.pdf Fulltext (4,64 MB)

Map matching with sparse cellular fingerprint observations
Chris Marshall, Federico Pittino, Andrea Dalla Torre, Angelo Montanari, Paolo Gallo, Donatella Gubiani, Andrea Viel, 2018, published scientific conference contribution

Found in: osebi
Keywords: map matching, fingerprint, trajectories
Published: 09.12.2018; Views: 2086; Downloads: 0
.pdf Fulltext (2,85 MB)

A map-matching algorithm dealing with sparse cellular fingerprint observations
Andrea Viel, Federico Pittino, Angelo Montanari, Chris Marshall, Donatella Gubiani, Paolo Gallo, Andrea Dalla Torre, 2019, original scientific article

Abstract: The widespread availability of mobile communication makes mobile devices a resource for the collection of data about mobile infrastructures and user mobility. In these contexts, the problem of reconstructing the most likely trajectory of a device on the road network on the basis of the sequence of observed locations (map-matching problem) turns out to be particularly relevant. Different contributions have demonstrated that the reconstruction of the trajectory of a device with good accuracy is technically feasible even when only a sparse set of GNSS positions is available. In this paper, we face the problem of coping with sparse sequences of cellular fingerprints. Compared to GNSS positions, cellular fingerprints provide coarser spatial information, but they work even when a device is missing GNSS positions or is operating in an energy saving mode. We devise a new map-matching algorithm, that exploits the well-known Hidden Markov Model and Random Forests to successfully deal with noisy and sparse cellular observations. The performance of the proposed solution has been tested over a medium-sized Italian city urban environment by varying both the sampling of the observations and the density of the fingerprint map as well as by including some GPS positions into the sequence of fingerprint observations.
Found in: osebi
Keywords: Map-matching algorithm, trajectory, cellular fingerprint, Hidden Markov Model
Published: 11.06.2019; Views: 1828; Downloads: 61
.pdf Fulltext (3,93 MB)

A cellular network database for fingerprint positioning systems
Angelo Montanari, Andrea Dalla Torre, Andrea Viel, Paolo Gallo, Donatella Gubiani, 2019, published scientific conference contribution

Abstract: Besides being a fundamental infrastructure for communication, cellular networks are increasingly exploited for positioning via signal fingerprinting. Here, we focus on cellular signal fingerprinting, where an accurate and comprehensive knowledge of the network is fundamental. We propose an original multilevel database for cellular networks, which can be automatically updated with new fingerprint measurements and makes it possible to execute a number of meaningful analyses. In particular, it allows one to monitor the distribution of cellular networks over countries, to determine the density of cells in different areas, and to detect inconsistencies in fingerprint observations.
Found in: osebi
Keywords: Cellular network, Signal fingerprinting, Multilevel database, Data analysis
Published: 17.09.2019; Views: 1763; Downloads: 0
.pdf Fulltext (2,40 MB)

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