Limits...
MagicFinger: 3D Magnetic Fingerprints for Indoor Location.

Carrillo D, Moreno V, Úbeda B, Skarmeta AF - Sensors (Basel) (2015)

Bottom Line: The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy.Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors.These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Communications Engineering, University of Murcia, 30100 Murcia, Spain. daniel.carrillo2@um.es.

ABSTRACT
Given the indispensable role of mobile phones in everyday life, phone-centric sensing systems are ideal candidates for ubiquitous observation purposes. This paper presents a novel approach for mobile phone-centric observation applied to indoor location. The approach involves a location fingerprinting methodology that takes advantage of the presence of magnetic field anomalies inside buildings. Unlike existing work on the subject, which uses the intensity of magnetic field for fingerprinting, our approach uses all three components of the measured magnetic field vectors to improve accuracy. By using adequate soft computing techniques, it is possible to adequately balance the constraints of common solutions. The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy. The proposed system consists of two phases: the offline phase and the online phase. In the offline phase, magnetic field measurements are taken throughout the building, and 3D maps are generated. Then, during the online phase, the user's location is estimated through the best estimator for each zone of the building. Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors. These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

No MeSH data available.


Related in: MedlinePlus

Comparison between classification based on features selected from Building A and B.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4541928&req=5

f11-sensors-15-17168: Comparison between classification based on features selected from Building A and B.

Mentions: Once the data were grouped into zones, the classifier is trained, and the best estimator for each zone is selected. To train the classifier, we choose the Gaussian process, using the variables selected by the Recursive Feature Elimination algorithm. At this point, it is interesting to analyze the performance of this classifier using the features selected for Building A and performing feature selection on data from Building B. As Figure 11 shows and as expected, using the best features for each building yields better results, although there is not a great difference. Furthermore, it is interesting to note that not all zones are correctly predicted, as happened in Building A. Here, classification and estimation is only feasible in Zones 2, 3, 5, 6 and 7. The features selected in Building B are the following: Bx, By entropy; Bx, By, Bz intensity; Bx, By kurtosis; Bx, By, Bz skewness; Bz sumPowerDetCoeff; Bz VarFFT. Considering these zones, classification yields the results summarized in Table 8.


MagicFinger: 3D Magnetic Fingerprints for Indoor Location.

Carrillo D, Moreno V, Úbeda B, Skarmeta AF - Sensors (Basel) (2015)

Comparison between classification based on features selected from Building A and B.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541928&req=5

f11-sensors-15-17168: Comparison between classification based on features selected from Building A and B.
Mentions: Once the data were grouped into zones, the classifier is trained, and the best estimator for each zone is selected. To train the classifier, we choose the Gaussian process, using the variables selected by the Recursive Feature Elimination algorithm. At this point, it is interesting to analyze the performance of this classifier using the features selected for Building A and performing feature selection on data from Building B. As Figure 11 shows and as expected, using the best features for each building yields better results, although there is not a great difference. Furthermore, it is interesting to note that not all zones are correctly predicted, as happened in Building A. Here, classification and estimation is only feasible in Zones 2, 3, 5, 6 and 7. The features selected in Building B are the following: Bx, By entropy; Bx, By, Bz intensity; Bx, By kurtosis; Bx, By, Bz skewness; Bz sumPowerDetCoeff; Bz VarFFT. Considering these zones, classification yields the results summarized in Table 8.

Bottom Line: The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy.Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors.These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Communications Engineering, University of Murcia, 30100 Murcia, Spain. daniel.carrillo2@um.es.

ABSTRACT
Given the indispensable role of mobile phones in everyday life, phone-centric sensing systems are ideal candidates for ubiquitous observation purposes. This paper presents a novel approach for mobile phone-centric observation applied to indoor location. The approach involves a location fingerprinting methodology that takes advantage of the presence of magnetic field anomalies inside buildings. Unlike existing work on the subject, which uses the intensity of magnetic field for fingerprinting, our approach uses all three components of the measured magnetic field vectors to improve accuracy. By using adequate soft computing techniques, it is possible to adequately balance the constraints of common solutions. The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy. The proposed system consists of two phases: the offline phase and the online phase. In the offline phase, magnetic field measurements are taken throughout the building, and 3D maps are generated. Then, during the online phase, the user's location is estimated through the best estimator for each zone of the building. Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors. These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

No MeSH data available.


Related in: MedlinePlus