An Energy Efficient Pedestrian Heading Estimation Algorithm using Smartphones

Nowadays, almost every smartphone is equipped with various inertial sensors, such as accelerometer, gyroscope, magnetometer and so on, which makes it possible to implement Pedestrian Dead Reckoning (PDR) on smartphones to assist in pedestrian positioning and tracking. However, in order to improve the accuracy of heading estimation involved in PDR, it is common to fuse the measurements of inertial sensors, which results in an inevitable increase in energy consumption of smartphones, thus weakening the endurance capacity of smartphones. In this paper, we present an energy efficient heading estimation algorithm based on Kalman filter with commercial off-the-shelf smartphones. To be specific, this algorithm makes use of the relatively accurate measurements of the gyroscope in the short time and the relatively stable measurements of the magnetometer in the long term, then reduces the sampling frequency of magnetometer and accelerometer to the one-sixth of that of gyroscope, finally fuses the asynchronous inertial measurements based on Kalman filter to produce heading estimate so as to save energy consumption without significantly sacrificing heading estimation accuracy. Extensive experiments are conducted and show that our proposed algorithm reduces the energy consumption by as much as 49.52% on average compared to the standard Kalman filter based algorithm, whereas achieving the similar accuracy of heading estimation.