Although active research has recently been conducted on received signal strength (RSS) fingerprint-based indoor localization, most of the current systems hardly overcome the costly and time-consuming offline training phase. In this paper, we propose an autonomous and collaborative RSS fingerprint collection and localization system. Mobile users track their position with inertial sensors and measure RSS from the surrounding access points. In this scenario, anonymous mobile users automatically collect data in daily life without purposefully surveying an entire building. The server progressively builds up a precise radio map as more users interact with their fingerprint data. The time drift error of inertial sensors is also compromised at run-time with the fingerprint-based localization, which runs with the collective fingerprints being currently built by the server. The proposed system has been implemented on a recent Android smartphone. The experiment results show that reasonable location accuracy is obtained with automatic fingerprinting in indoor environments.
|Number of pages
|IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
|Published - 2012 Jan
Bibliographical noteFunding Information:
Manuscript received July 23, 2010; revised October 28 2010; accepted November 10 2010. Date of publication December 23, 2010; date of current version December 16, 2011. This work was supported by the National Research Foundation of Korea under Grant 2010-0000405, funded by the Ministry of Education, Science, and Technology of the Korean government. This paper was recommended by Associate Editor L. B. Sheremetov.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering