A Continuous PDR and GNSS Fusing Algorithm for Smartphone Positioning

Abstract

Pedestrian dead reckoning (PDR), used in state-of-the-art smartphones, calculates pedestrian positions by using built-in inertial sensors. However, the complex and changeable usage modes of smartphones have been obstructing the development of PDR in the field of gait detection. Since the measurement of the sensor is affected by noise, position errors will emerge, needing to be corrected periodically via external measurements. To this end, an optimization-based PDR (OBPDR) method for smartphones is proposed in this study. First, an improved finite state machine (IFSM) gait detection method is designed, which can improve the gait recognition rate and stability compared with the traditional peak detection method. Second, the step detection algorithm proposed in this paper is combined with a heading estimation to obtain the PDR dynamic model. Finally, the measurements of GNSS are fused to the PDR model, based on an adaptive extended Kalman filter (AEKF) algorithm, which can enhance the adaptability of the system to the environment in order to reduce the cumulative errors of PDR. Experiments are carried out to evaluate the performance of the proposed method. The results indicate that compared with the gait detection method, based on peak detection, and the integrated positioning method, based on an extended Kalman filter, the proposed method boasts favorable robustness and a high gait recognition rate, the recognition accuracy being kept between 97.5% and 98.5%; the average position error decreased by more than 67.25%.

Research on Pedestrian Indoor Positioning Based on Two-Step Robust Adaptive Cubature Kalman Filter with Smartphone MEMS Sensors

“…, , , q q q q q q qi q j q k = + + + (1) where q0, q1, q2, and q3 are real numbers, and i, j, and k are unit vectors.…”

“…The zero-crossing method completes the judgment of pedestrian stride with this law. At the same time, the following conditions should be met: (1) The peak is greater than the threshold value, (2) the trough is less than the threshold value, (3) the difference between the peak and the trough is greater than the threshold value, and (4) the time difference between adjacent peaks is greater than the threshold value.…”

“…With the rapid development of mobile devices, location-based service (LBS) has become increasingly important [ 1 ]. There has been a dramatic increase in pedestrian demand for LBS both indoors and outdoors.…”

An Effective GNSS/PDR Fusion Positioning Algorithm on Smartphones for Challenging Scenarios

“…A factor graph was constructed to represent the relationship between the status, measurement, and distance information of multiple agents, significantly improving the positioning accuracy under GNSS signal challenging conditions. In addition, Zhang et al [ 17 ] analyzed the positioning performance of the GNSS/PDR fusion algorithm on smartphones in complex environments. Yan et al [ 18 ] designed an improved Kalman filter based on smartphone GNSS and IMU data with different sampling frequencies.…”

Pedestrian Smartphone Navigation Based on Weighted Graph Factor Optimization Utilizing GPS/BDS Multi-Constellation

“…In recent decades, the demand for location-based services (LBS) has significantly increased, leading to the development of positioning technologies and systems [1]. The global navigation satellite system (GNSS) has been widely adopted for outdoor positioning [2][3][4]. Additionally, with the rapid advancement of mobile internet technology, smart devices have become increasingly important in the field of location-based services [5][6][7].…”