Mobile device’s PDR Application Using CNN Based SpeedNet and GNSS Fusion

In recent years, wearable sensors and mobile devices have become popular tools in the field of positioning. In pedestrian navigation, Pedestrian Dead Reckoning (PDR) is the primary algorithm, with numerous previous research cases available. However, traditional PDR algorithms' stride length calculations rely on empirical formulas that are influenced by factors such as user height, walking frequency, and walking habits. Without appropriate parameters, stride length estimation can result in significant errors, leading to poor positioning outcomes. Apart from the stride length calculation issue, IMUs contain bias and noise themselves, leading to drift errors over time, especially in consumer-grade IMUs. To address these issues, this study introduces a CNN velocity estimation model to calculate users' 1D velocity. The trained velocity estimation model can overcome the user dependency issue caused by traditional PDR because the training data covers different users. For the heading calculation, this study employs a novel 9D IMU AHRS algorithm (Laidig et al., 2022) to address attitude estimation problems that traditional PDR cannot handle effectively under high motion conditions. Finally, incorporating GNSS through the principles of Extended Kalman Filter(EKF) to compensate for IMU’s drift over time. In the experiment, we use NovAtel Pwrpak as ground truth. It contains high-quality GNSS and IMU, which can provide reliable reference trajectory. A comparison of trajectories is conducted using Huawei mate20 pro as our smartphone device in different modes.