Indoor navigation is now used in many applications. For enhanced positioning accuracy indoors, this thesis develops the strategies and algorithms for multiple navigational integration modes coupled with multi-sensors fusion in smartphones. The sensors consist of an INS-magnetometer triad and a pressure sensor, in addition to WiFi signals. The main focus is to make full use of the complementary features of different sensors and different navigation modes to improve accuracy. One of the sensors, namely pressure sensor, complements the limitations of PDR and WiFi by extending the position dimension from 2D to 3D.
A centralized Kalman filter is designed and implemented for the proposed integration strategies including PDR and INS, WiFi and INS, as well as combination of PDR, WiFi and INS. Extensive indoor tests conducted in various buildings are used to evaluate the above strategies. Related navigation performance metrics are evaluated and inter-compared and their error statistics are summarized.