Multi-sensor wireless location system with extended kalman filter

Abstract

The ability to determine the position of a device is of fundamental importance in context-aware and location dependent mobile computing. Typical indoor applications require better accuracy than what current outdoor location systems provide. Outdoor location technologies such as GPS have poor indoor performance because of the harsh nature of indoor environments. Further, typical indoor applications require different types of location information such as physical space, position and orientation. To obtain high precision, a variety of indoor location systems were proposed. However, existing location systems have some problems to use in nomadic environments. Specially, high precision in realistic pedestrian speed is an important key for nomadic applications. In this thesis, we describe the design and implementation of the multi-sensor wireless location system that provides accurate location in nomadic applications. Also, this thesis describes how proposed location system achieves accurate distance measurements between beacons and listeners with some novel algorithms: a median method and an outlier rejection method. We also describe an extended Kalman filter and define how to model location system based on an extended Kalman filter. Finally, the thesis suggests sensor fusion not only to obtain more accurate positioning but also to overcome NLOS problems with an accelerometer sensor.