Kalman filtering: theory and practice
Kalman filtering: theory and practice
Data Management in Location-Dependent Information Services
IEEE Pervasive Computing
A core model supporting location-aware computing in smart classroom
ICWL'05 Proceedings of the 4th international conference on Advances in Web-Based Learning
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For supporting location-aware computing in indoor environments, the location sensing/positioning system not only need to provide objects' precise location, but also should own such characteristics as: isotropy and convenience for portability. In this paper, we present an indoor location sensing system, Cicada. This System is based on the TDOA (time difference of arrival) between Radiofrequency and ultrasound to estimate distance, and adopts a technology integrating Slide Window Filter (SWF) and Extended Kalman Filter (EKF) to calculate location. Consequently, it not only can determine the coordinate location within 5cm average deviation either for static objects or for mobile objects, but also owns a nearly omni-directional working area. Moreover, it is able to run independently, mini and light so that it is very easy to be portable and even embedded into people's paraphernalia.