A MEMS Multi-Sensors System for Pedestrian Navigation

Micro-electro-mechanical system (MEMS) sensors are widely used in many applications due to their low cost, low power consumption, small size and light weight. Such MEMS sensors which are usually called multi-sensors include accelerometers, gyroscopes, magnetometers and barometers. In this research, Samsung Galaxy Note is used as the MEMS multi-sensors platform for pedestrian navigation. It contains a three-axis accelerometer, a three-axis gyroscope, a three-axis magnetometer and GPS receiver. Pedestrian Dead Reckoning (PDR) algorithms which include step detection, stride length estimation, heading estimation and PDR mechanization are carefully discussed in this paper. GPS solution is the major aiding source to reduce the MEMS IMU position, velocity and attitude errors when GPS signals are available. Magnetometers are also used to reduce the attitude errors of gyroscopes if there are no environment disturbances. A loosely-coupled extended Kalman Filter is implemented in the paper to fuse all the information to obtain the position result. Two typical scenarios are tested and analyzed in this paper: walking from outdoor to indoor and indoor walking. The MEMS multi-sensors system works well for both scenarios. To conclude, algorithms of MEMS multi-sensors system can provide an accurate, reliable and continuous result for pedestrian navigation on the platform of smart phone.