In this thesis, a hybrid positioning system is developed, which combines GPS, Dead Reckoning (DR) and Signpost technologies for the purpose of improving the Toronto Transit System bus service. The DR system is used as the main positioning system, while GPS and Signpost systems are used as aiding systems to compensate for the DR errors. The integration is done in the position domain, which simplifies the system design. A centralized Kalman filter with five states is developed to combine all the available measurements. Field tests have been designed and conducted to assess the system design and performance under various environmental conditions. It is shown that the achieved positioning accuracy of the integrated DR/GPS/Signpost system is at the few metres level in unobstructed environments. In addition, despite the signal obstruction in the downtown area, the positioning solution of the integrated system is still sufficiently precise.
Dynamic Tracking Algorithm for Time-Varying Neuronal Network Connectivity using Wide-Field Optical Image Video Sequences