A Dynamic Indoor Location Model for Smartphones Based on Magnetic Field: A Preliminary Approach

2016 ◽  
pp. 260-269
Author(s):  
Carlos E. Galván-Tejada ◽  
Jorge I. Galván-Tejada ◽  
José M. Celaya-Padilla ◽  
J. Rubén Delgado-Contreras ◽  
Vanessa Alcalá-Ramírez ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Location Model ◽  
Indoor Location

scholarly journals Fingerprint Time Length Reduction for Developing an Indoor Location Model for Smartphones

2014 ◽  
Vol 37 ◽  
pp. 32-39 ◽  
Author(s):  
Carlos E. Galván-Tejada ◽  
Juan P. Garćıa-Vázquez ◽  
Jorge I. Galván-Tejada ◽  
Juan R. Delgado-Contreras ◽  
Ramon F. Brena
Keyword(s):  
Location Model ◽  
Indoor Location ◽  
Time Length ◽  
Length Reduction

scholarly journals INDOOR SPACE LOCATION MODEL BASED ON LOCATION SERVICE

2017 ◽  
Vol XLII-4/W7 ◽  
pp. 49-53
Author(s):  
Y. Zhou ◽  
G. Zeng ◽  
Y. Huang ◽  
X. Yang
Keyword(s):  
Spatial Information ◽  
Indoor Navigation ◽  
Location Model ◽  
Location Information ◽  
Location Based Service ◽  
Location Services ◽  
Indoor Location ◽  
Indoor Space ◽  
Location Service ◽  
Space Model

Location is the basis for the realization of location services, the integrity of the location information and its way of representation in indoor space model directly restricts the quality of location services. The construction of the existing indoor space model is mostly for specific applications and lack of uniform representation of location information. Several geospatial standards have been developed to meet the requirement of the indoor spatial information system, among which CityGML LOD4 and IndoorGML are the most relevant ones for indoor spatial information. However, from the perspective of Location Based Service (LBS), the CityGML LOD4 is more inclined to visualize the indoor space. Although IndoorGML is mainly used for indoor space navigation and has description (such as geometry, topology, and semantics) benefiting for indoor LBS, this standard model lack explicit representation of indoor location information. In this paper, from the perspective of Location Based Service (LBS), based on the IndoorGML standard, an indoor space location model (ISLM) conforming to human cognition is proposed through integration of the geometric and topological and semantic features of the indoor spatial entity. This model has the explicit description of location information which the standard indoor space model of IndoorGML and CityGML LOD4 does not have, which can lay the theoretical foundation for indoor location service such as indoor navigation, indoor routing and location query.

scholarly journals Magnetic Field Feature Extraction and Selection for Indoor Location Estimation

Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 11001-11015 ◽  
Author(s):  
Carlos Galván-Tejada ◽  
Juan García-Vázquez ◽  
Ramon Brena
Keyword(s):  
Magnetic Field ◽  
Feature Extraction ◽  
Location Estimation ◽  
Indoor Location ◽  
Feature Extraction And Selection ◽  
Selection For ◽  
Field Feature

scholarly journals Indoor Localization through Mobile Participatory Sensing and Magnetic Field

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 70
Author(s):  
Juan Vázquez ◽  
Isabel Silva
Keyword(s):  
Magnetic Field ◽  
Indoor Localization ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Participatory Sensing ◽  
Kappa Statistics ◽  
K Nearest Neighbors ◽  
Indoor Location ◽  
Localization Model

Development of indoor location systems that use smartphone sensors has been a topic of interest to industry and academia. In this paper, we describe an experiment that was performed to evaluate the feasibility of creating a mobile indoor localization model based on data from participatory sensing. To achieve it, seven smartphone users used their integrated magnetometers to collected magnetic field information on a building. The data collected are utilized to train three machine learning algorithms: The k Nearest Neighbors (KNN), Decision Trees (J48), and Naïve Bayes algorithms. The performance of the algorithms was measured through the accuracy and kappa statistics. Our results indicate that it is possible to create an infrastructure-less indoor localization model at room level using data from participatory sensing. The model with the most significant performance was obtained with the KNN, since it offers an accuracy of 97.12%, while the model with the most reduced performance was Naïve Bayes, since it offers an accuracy of 50.79%.

scholarly journals Toward improving indoor magnetic field–based positioning system using pedestrian motion models

2018 ◽  
Vol 14 (9) ◽  
pp. 155014771880307 ◽  
Author(s):  
Wenhua Shao ◽  
Haiyong Luo ◽  
Fang Zhao ◽  
Antonino Crivello
Keyword(s):  
Magnetic Field ◽  
Real Time ◽  
Service Providers ◽  
Low Cost ◽  
Low Frequency ◽  
Location Based Services ◽  
Fingerprint Matching ◽  
Indoor Location ◽  
Motion Models ◽  
The Magnetic Field

Indoor magnetic field has attracted considerable attention in indoor location–based services, because of its pervasive and stable attributes. Generally, in order to harness the location features of the magnetic field, particle filters are introduced to simulate the possibilities of user locations. Real-time magnetic field fingerprints are matched with model fingerprints to adjust the location possibilities. However, the computation overheads of the magnetic matching are rather high, thus limiting their applications to mobile computing platforms and indoor location–based service providers that serve massive users. In order to reduce the computation overhead, the article presents a low-cost magnetic field fingerprint matching scheme. Based on the low-frequency features of the magnetic field, the scheme updates particle weights according to the mass center of the magnetic field deltas of pedestrian steps. The proposed low-cost scheme decreases the complexity of real-time fingerprints without harming the positioning performance. In order to further improve the positioning accuracy, not asking users to hold the smartphone in fixed attitudes, we also present a smartphone attitude detection method that enables the proposed scheme to automatically select proper fingerprints. Experiments convincingly reveal that the proposed scheme achieves about 1 m accuracy at 80% with low computation overheads.

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