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Collaborative WiFi Fingerprinting Using Sensor-Based Navigation on Smartphones.

Zhang P, Zhao Q, Li Y, Niu X, Zhuang Y, Liu J - Sensors (Basel) (2015)

Bottom Line: Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database.Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method.Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications.

View Article: PubMed Central - PubMed

Affiliation: GNSS Research Center, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China. fenix@whu.edu.cn.

ABSTRACT
This paper presents a method that trains the WiFi fingerprint database using sensor-based navigation solutions. Since micro-electromechanical systems (MEMS) sensors provide only a short-term accuracy but suffer from the accuracy degradation with time, we restrict the time length of available indoor navigation trajectories, and conduct post-processing to improve the sensor-based navigation solution. Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database. Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method. Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications.

No MeSH data available.


Sensor-based navigation solutions (a) Trajectory 1; (b) Trajectory 2; (c) Trajectory 3; (d) Trajectory 4.
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sensors-15-17534-f005: Sensor-based navigation solutions (a) Trajectory 1; (b) Trajectory 2; (c) Trajectory 3; (d) Trajectory 4.

Mentions: Figure 5a–d shows the sensor-based navigation solutions in a local geographic frame. In each figure, the blue dash line, green dashed line, red solid line, and black dotted line are the forward, backward, smoothed result, and the reference. The reference is provided by correcting the PDR solution aided by floor plan (i.e., the true position of corners and intersections, and the true orientation of corridors). The floor plan information was obtained from Google Earth. The start and end points indicates the starting points of forward and backward PDR, respectively.


Collaborative WiFi Fingerprinting Using Sensor-Based Navigation on Smartphones.

Zhang P, Zhao Q, Li Y, Niu X, Zhuang Y, Liu J - Sensors (Basel) (2015)

Sensor-based navigation solutions (a) Trajectory 1; (b) Trajectory 2; (c) Trajectory 3; (d) Trajectory 4.
© Copyright Policy
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4541948&req=5

sensors-15-17534-f005: Sensor-based navigation solutions (a) Trajectory 1; (b) Trajectory 2; (c) Trajectory 3; (d) Trajectory 4.
Mentions: Figure 5a–d shows the sensor-based navigation solutions in a local geographic frame. In each figure, the blue dash line, green dashed line, red solid line, and black dotted line are the forward, backward, smoothed result, and the reference. The reference is provided by correcting the PDR solution aided by floor plan (i.e., the true position of corners and intersections, and the true orientation of corridors). The floor plan information was obtained from Google Earth. The start and end points indicates the starting points of forward and backward PDR, respectively.

Bottom Line: Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database.Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method.Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications.

View Article: PubMed Central - PubMed

Affiliation: GNSS Research Center, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China. fenix@whu.edu.cn.

ABSTRACT
This paper presents a method that trains the WiFi fingerprint database using sensor-based navigation solutions. Since micro-electromechanical systems (MEMS) sensors provide only a short-term accuracy but suffer from the accuracy degradation with time, we restrict the time length of available indoor navigation trajectories, and conduct post-processing to improve the sensor-based navigation solution. Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database. Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method. Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications.

No MeSH data available.