Abstract
With the popularity of smartphones and the development of microelectromechanical system (MEMS), the pedestrian dead reckoning (PDR) algorithm based on the built-in sensors of a smartphone has attracted much research. Heading estimation is the key to obtaining reliable position information. Hence, an adaptive Kalman filter (AKF) based on an autoregressive model (AR) is proposed to improve the accuracy of heading for pedestrian dead reckoning in a complex indoor environment. Our approach uses an autoregressive model to build a Kalman filter (KF), and the heading is calculated by the gyroscope, obtained by the magnetometer, and stored by previous estimates, then are fused to determine the measurement heading. An AKF based on the innovation sequence is used to adaptively adjust the state variance matrix to enhance the accuracy of the heading estimation. In order to suppress the drift of the gyroscope, the heading calculated by the AKF is used to correct the heading calculated by the outputs of the gyroscope if a quasi-static magnetic field is detected. Data were collected using two smartphones. These experiments showed that the average error of two-dimensional (2D) position estimation obtained by the proposed algorithm is reduced by 40.00% and 66.39%, and the root mean square (RMS) is improved by 43.87% and 66.79%, respectively, for these two smartphones.
Highlights
Location-based-services (LBSs) have developed rapidly in recent years, and their application brings great convenience to people’s lives [1]
A novel heading prediction based on an autoregressive model (AR) prediction model is introduced to Kalman filter (KF), and the current heading—determined by fusing these headings calculated by the gyroscope, obtained by the magnetometer and stored by the previous estimate—is used as the measurement of KF
Where βprev, βmag, and β gyro are the respective weights of the heading stored by the previous estimate, the heading calculated by the outputs of the current magnetometer and the heading obtained mag gyro by the gyroscope—ψk is the current heading calculated by Equation (26), ψk is the current prev heading calculated by Equation (31), ψk−1 is the heading of previous time updated by KF, ψcor and δ mag ψδ —are thresholds
Summary
Location-based-services (LBSs) have developed rapidly in recent years, and their application brings great convenience to people’s lives [1]. SINS for a foot-mounted inertial pedestrian navigation system (PNS), with 0.3% accuracy of location estimation for walking distance. These methods require extra MEMS sensors, which are not suitable for LBSs based on a smartphone’s built-in sensors. Rennaudin et al [24] applied the acceleration, magnetic field strength in QS, and the outputs of gyroscope to estimate the heading by extended Kalman filter (EKF). A novel heading prediction based on an autoregressive model (AR) prediction model is introduced to KF, and the current heading—determined by fusing these headings calculated by the gyroscope, obtained by the magnetometer and stored by the previous estimate—is used as the measurement of KF. Taking into account model function error and inaccurate prior noise, AKF is used to improve the accuracy of heading estimation.
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