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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.


WiFi fingerprinting result using proposed DB (a) and that using reference DB (b).
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sensors-15-17534-f018: WiFi fingerprinting result using proposed DB (a) and that using reference DB (b).

Mentions: In this WiFi positioning test, we use the same trajectory as the DB training tests, but walked in the opposite direction. The WiFi fingerprinting results with the proposed DB and the conventional DB were shown in Figure 18a,b 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)

WiFi fingerprinting result using proposed DB (a) and that using reference DB (b).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17534-f018: WiFi fingerprinting result using proposed DB (a) and that using reference DB (b).
Mentions: In this WiFi positioning test, we use the same trajectory as the DB training tests, but walked in the opposite direction. The WiFi fingerprinting results with the proposed DB and the conventional DB were shown in Figure 18a,b 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.