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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.


Error distances of sensor-based navigation solutions (a) Trajectory 1; (b) Trajectory 2; (c) Trajectory 3; (d) Trajectory 4.
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sensors-15-17534-f006: Error distances of sensor-based navigation solutions (a) Trajectory 1; (b) Trajectory 2; (c) Trajectory 3; (d) Trajectory 4.

Mentions: Figure 5 shows that both forward and backward PDR trajectories had a similar shape with the reference and are accurate at the beginning, but suffered from long-term drifts. The drifts were caused by both heading and step length errors, which are the issues inherent in the sensors-only navigation algorithm. The smoothed trajectory significantly got closed to the reference at beginning and ending periods since forward and backward PDR solutions were accurate and had high weight at beginning and ending periods, respectively. To make the errors clear, the error distances (i.e., the distance between estimated user position and the corresponding true position) of these solutions are illustrated in Figure 6a–d. In each figure, the blue dashed line, green dotted line, and red solid line represent the error distances of forward, backward, and smoothing result, respectively. The magenta solid line and cyan dashed line indicate the RMS values of forward and smoothed results.


Collaborative WiFi Fingerprinting Using Sensor-Based Navigation on Smartphones.

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

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

License
Show All Figures
getmorefigures.php?uid=PMC4541948&req=5

sensors-15-17534-f006: Error distances of sensor-based navigation solutions (a) Trajectory 1; (b) Trajectory 2; (c) Trajectory 3; (d) Trajectory 4.
Mentions: Figure 5 shows that both forward and backward PDR trajectories had a similar shape with the reference and are accurate at the beginning, but suffered from long-term drifts. The drifts were caused by both heading and step length errors, which are the issues inherent in the sensors-only navigation algorithm. The smoothed trajectory significantly got closed to the reference at beginning and ending periods since forward and backward PDR solutions were accurate and had high weight at beginning and ending periods, respectively. To make the errors clear, the error distances (i.e., the distance between estimated user position and the corresponding true position) of these solutions are illustrated in Figure 6a–d. In each figure, the blue dashed line, green dotted line, and red solid line represent the error distances of forward, backward, and smoothing result, respectively. The magenta solid line and cyan dashed line indicate the RMS values of forward and smoothed results.

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.