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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.


The DB built through proposed method (a), and the reference DB built by using the conventional floor plan aided training approach (b).
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sensors-15-17534-f007: The DB built through proposed method (a), and the reference DB built by using the conventional floor plan aided training approach (b).

Mentions: The max and RMS values of error distance were generally reduced from 16.1 m and 8.7 m to 9.6 m and 5.7 m after smoothing, with a reduction of 34.5%. Then, the smoothed navigation solutions were used to build the WiFi fingerprint DB. The DB built through the proposed method (which will be denoted as “the proposed DB” for short) is shown in Figure 7a. To make a comparison, a reference DB was built by using the conventional floor plan aided training approach (which will be denoted as “the reference DB” for short) and shown in Figure 7b. It is clear that the proposed DB has some shifts when comparing with the true path, while the reference DB fits the true path. We will evaluate the effect of such DB shift on WiFi positioning errors by WiFi positioning tests in the next subsection.


Collaborative WiFi Fingerprinting Using Sensor-Based Navigation on Smartphones.

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

The DB built through proposed method (a), and the reference DB built by using the conventional floor plan aided training approach (b).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17534-f007: The DB built through proposed method (a), and the reference DB built by using the conventional floor plan aided training approach (b).
Mentions: The max and RMS values of error distance were generally reduced from 16.1 m and 8.7 m to 9.6 m and 5.7 m after smoothing, with a reduction of 34.5%. Then, the smoothed navigation solutions were used to build the WiFi fingerprint DB. The DB built through the proposed method (which will be denoted as “the proposed DB” for short) is shown in Figure 7a. To make a comparison, a reference DB was built by using the conventional floor plan aided training approach (which will be denoted as “the reference DB” for short) and shown in Figure 7b. It is clear that the proposed DB has some shifts when comparing with the true path, while the reference DB fits the true path. We will evaluate the effect of such DB shift on WiFi positioning errors by WiFi positioning tests in the next subsection.

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.