Pedestrian Dead Reckoning Using Pocket-Worn Smartphone

Indoor pedestrian dead reckoning (PDR) extends the location-based service (LBS) to environments, where GPS or beacon signals are degraded or unavailable. A practical PDR system should consider the absence of any infrastructure or prior knowledge of the environment. This paper presents a PDR system based on a pocket-worn smartphone, which tracks a person's location through dead reckoning calculation by using the sensors embedded in smartphones. However, a smartphone-based PDR system faces various challenges, especially the heading drift due to gyroscope bias. In this paper, the gradient descent algorithm (GDA) is improved to reduce the heading drift, by fusing inertial data with only a fraction of magnetometer data that are accurate and usable. There is an 80% probability that the heading error is reduced to less than 4 degrees. Besides, a stride detection method is developed based on thigh angles, and then, a stride length estimation method is implemented in a complementary way. The experiments were conducted along with three types of reference paths, and the experimental results demonstrate that the average position errors along the three paths are 1.62%, 1.00%, and 0.92%, respectively. Despite the inherent sensor noise and complex human locomotion, the smartphone-based PDR system has great potential in pedestrian tracking.