A CNN-SPEED-BASED GNSS/PDR INTEGRATED SYSTEM FOR SMARTWATCH
Keywords: GNSS, Pedestrian Dead Reckoning, CNN-Speed-Model, Extended Kalman Filter, Smartwatch
Abstract. In recent years, wearable devices such as smart bands and smartwatches have gained widespread popularity due to their ability to provide various health and fitness applications by detecting and analyzing the human body and motion information. However, the accuracy of location-based services can be limited, especially in urban areas and indoors. This study proposes a series of smartwatch Pedestrian Dead Reckoning (PDR) improvements based on 9 Degrees of Freedom (DOF) IMU orientation estimation, which includes the heading estimation of human movement and a novel pre-trained velocity regression model. The proposed system holds the potential to enhance positioning accuracy and augment navigation availability for smartwatch users, thus offering potential applications across various fields. This study makes significant contributions to the field of smartwatch navigation by proposing a GNSS/PDR fusion algorithm specifically designed for the consumer-grade IMU, magnetometer, and GNSS receiver built into Apple Watch, tracking varied roll and pitch of the sensor caused by hand swing, and integrating a CNN model to predict the 1-D speed and provide ZUPT information, offering improved accuracy and reliability.