Robust Pedestrian Dead Reckoning Integrating Magnetic Field Signals and Digital Terrestrial Multimedia Broadcasting Signals

Abstract

Currently, many positioning technologies complementary to Global Navigation Satellite System (GNSS) are providing ubiquitous positioning services, especially the coupling positioning of Pedestrian Dead Reckoning (PDR) and other signals. Magnetic field signals are stable and ubiquitous, while Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong penetration and stable transmission over a large range. To improve the positioning performance of PDR, this paper proposes a robust PDR integrating magnetic field signals and DTMB signals. In our study, the Spiking Neural Network (SNN) is first used to learn the magnetic field signals of the environment, and then the learning model is used to detect the magnetic field landmarks. At the same time, the DTMB signals are collected by the self-developed signal receiver, and then the carrier phase ranging of the DTMB signals is realized. Finally, robust pedestrian positioning is achieved by integrating position information from magnetic field landmarks and ranging information from DTMB signals through Extended Kalman Filter (EKF). We have conducted indoor and outdoor field tests to verify the proposed method, and the outdoor field test results showed that the positioning error cumulative distribution of the proposed method reaches 2.84 m at a 68% probability level, while that of the PDR only reaches 8.77 m. The proposed method has been validated to be effective and has good positioning performance, providing an alternative solution for seamless indoor and outdoor positioning.