Paper
A Robust and Unsupervised RSS-based Localization System in WLAN Environments
- Authors:
- Cheyun Xia; Yuan Li; Wai-Choong Wong; Lei Wang
- Abstract
- With the proliferation of location based services (LBS), various indoor localization systems have been proposed based on received signal strength (RSS). Many existing infrastructures of wireless local area networks (WLANs) have been deployed for widespread communication coverage. Hence, one mobile device may receive signals from only one or two official access points (APs), which renders the conventional localization systems impractical. However, many unknown wireless APs are often perceivable and can be utilized by the RSS fingerprint approach, which suffers from tremendous training costs and device diversity. With this motivation, this paper proposes a robust and cost-effective localization system to mitigate the effects of device diversity as well as reduce the training costs by employing two algorithms: a power-gap elimination algorithm and an unsupervised training algorithm. Simulation and experimental results demonstrate that the mean error of the proposed localization system is approximately five meters under various conditions, and the mean error of using supervised RSS fingerprints is 3.2 meters.
- Keywords
- Robust Localization; Unsupervised; Fingerprinting; Calibration-free; Device Diversity
- StartPage
- 55
- EndPage
- 69
- Doi