Information filter with speed detection for indoor Bluetooth positioning

This paper studies indoor Bluetooth network. wireless positioning using a network of Bluetooth signals. Fingerprints of received signal strength indicators are used for localization. Due to the relatively long interval between the available consecutive Bluetooth signal strength measurements, we propose a method of information filtering with speed detection, which combines the estimation information from the received signal strength (RSS) measurements with the prior information from the motion model. Speed detection is further assisted to correct the outliers of position estimation. The field tests show that the new algorithm proposed applying information filter with speed detection improves the horizontal positioning accuracy of indoor navigation with about 17% compared to the static fingerprinting positioning method, achieving a 4.2 m positioning accuracy on the average, and about 16% improvement compared to the point Kalman filter.