The growing demand for indoor navigation applications has promoted the implementation of navigation techniques on handheld devices. An accurate and reliable indoor navigation system hosted on handheld devices would benefit many consumer industries. MEMS (Micro-Electromechanical System) sensors can provide a short-term accurate navigation solution. WiFi-based (Wireless Fidelity) positioning is another potential technology for indoor navigation, which only uses pre-existing WiFi infrastructures and is a good source to aid the MEMS-based navigation solution. However, WiFi positioning requires databases to estimate the user position. The pre-surveys for building and maintaining the WiFi databases make most current WiFi positioning systems are not automatic. Currently, it remains difficult to find an automatic and accurate indoor navigation system on typical handheld devices. However, the complementary characteristics of MEMS sensors and WiFi offer an efficient integration for indoor navigation applications.
Two automatic WiFi Positioning Services (WPSs) based on trilateration and fingerprinting are investigated in this research, which both consist of the background survey service and WiFi positioning service. Both WPSs provide WiFi positioning solutions, with no cost to build and to maintain WiFi databases. This removes the limitations that most current WPSs require time-consuming and labor-intensive pre-surveys to build the databases. Different approaches are investigated to improve the accuracy of both the WiFi databases and the user’s positions in indoor environments. The developed two automatic WPSs are also compared.
An innovative MEMS navigation solution, based on motion constraints and the integration of INS (Inertial Navigation System) and PDR (Pedestrian Dead Reckoning), is built on handheld devices. LC (Loosely-coupled) integration and TC (Tightly-coupled) integration are implemented for WiFi and MEMS sensors to further limit the drifts of MEMS sensors. The navigation performances of PDR, INS, the PDR/INS-integrated MEMS solution, the LC integration solution, and the TC integration solution are compared in this research. The test results also show its average positioning error of TC integration in various trajectories is 0.01% of INS, 10.38% of PDR, 32.11% of the developed MEMS solution, and 64.58% of LC integration. This developed TC integration solution can be used in both environments with dense and sparse deployments of WiFi APs (Access Points).