In this thesis we present a method of indoor localization and tracking that combines multiple sensor measurements to remove dependence on any one information source. A two-step process is proposed that performs an initial localization estimate, followed by particle filter based tracking. Initial localization is performed using WiFi and image observations. For tracking we fuse information from WiFi, magnetic, and inertial sensors. We demonstrate the feasibility of this system using fingerprint maps that are collected with a single walkthrough of the building at normal walking pace. In addition to a smartphone or tablet, only a foot mounted inertial measurement unit (IMU) is needed for database generation. Only a smartphone is needed for positioning after database generation. The positioning method presented uses sensors available on most mobile devices and requires no new infrastructure to be placed in the building. We present results for two locations: the Stoneridge Mall in Pleasanton, California, and the Doe Library at the UC Berkeley campus. We achieve an average location error of 2.6m across both locations.