Abstract
Numerous solutions/methods to solve the existing problems of pedestrian navigation/localization have been proposed in the last decade by both industrial and academic researchers. However, to date there are still major challenges for a single pedestrian navigation system (PNS) to operate continuously, robustly, and seamlessly in all indoor and outdoor environments. In this paper, a novel method for pedestrian navigation approach to fuse the information from two separate PNSs is proposed. When both systems are used at the same time by a specific user, a nonlinear inequality constraint between the two systems’ navigation estimates always exists. Through exploring this constraint information, a novel filtering technique named Kalman filter with state constraint is used to diminish the positioning errors of both systems. The proposed method was tested by fusing the navigation information from two different PNSs, one is the foot-mounted inertial navigation system (INS) mechanization-based system, the other PNS is a navigation device that is mounted on the user’s upper body, and adopting the pedestrian dead reckoning (PDR) mechanization for navigation update. Monte Carlo simulations and real field experiments show that the proposed method for the integration of multiple PNSs could improve each PNS’ navigation performance.
Highlights
The last few years have witnessed major progress in the development of pedestrian navigation systems (PNS)
Available environments illustrating how the proposed algorithm works was conducted, the basic experiment was done in a Global Navigation SatelliteSystem (GNSS) available area
The multiple paths-based experiments were done in a GNSS-denied indoor environment
Summary
The last few years have witnessed major progress in the development of pedestrian navigation systems (PNS). Common users of PNS mainly include the following: First responders (e.g., emergency search and rescue personnel, police and military forces). MEMS inertial sensors, including accelerometers, gyroscopes, magnetometers, and barometers, are usually called self-contained sensors or inertial measurement units (IMU). Most of the sensors mentioned above can be found in various consumer products, especially in the well-developed market for smart devices such as smart phones, smart watches, smart glasses, tablet computers, etc. Based on these sensors and devices, numerous researchers have been dedicated to PNS research. To date there are still major challenges for PNS to continuously, robustly, and seamlessly operate in all indoor and outdoor environments
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