In this paper, we proposed an algorithm that improves the heading accuracy of the global positioning system (GPS)/inertial navigation system (INS) integrated system used in automobiles by manipulating INS velocity and GPS velocity measurements. Two velocities are provided by the GPS receiver: the velocity calculated using the position difference and the velocity calculated using the Doppler shift. The velocity obtained using the position difference is an average velocity for a certain time period, which is inaccurate under dynamic conditions because of its time delay. In contrast, the velocity from the Doppler shift is an instantaneous velocity and has no time delay. However, it also relies on pseudo-range error and noise, which degrades the heading estimation accuracy in low dynamic situations. Thus, although the velocity measurements can improve heading accuracy, the navigation performance is often degraded when the velocity measurements are used for GPS/INS integration. To improve the heading accuracy by solving the aforementioned problems, we proposed a heading accuracy improvement algorithm that employs the average velocity measurements obtained using the averaged GPS velocity and the average velocity of the INS. Since the proposed average velocity measurements are calculated using long baseline, the proposed algorithm can improve the heading accuracy without using other sensors, especially in the case of low dynamic situations. It can be easily applied to the existing GPS/INS integrated system, making it suitable for use in automotive navigation systems. In this research, it is verified that the average velocity measurements can be substituted for Kalman filter measurements, and the performance improvements are confirmed through simulations and experiments. INDEX TERMS Automobile vehicles navigation, GPS integrated navigation, velocity, heading accuracy, average velocity measurements.
Environments with varying magnetic field distortion cannot be navigated stably with magnetic anomaly based navigation algorithms. In this study, we propose a stable navigation solution for various indoor environments by fusing magnetic anomaly matched trajectories and mobile robot inertial trajectories. The proposed method uses dead reckoning as the primary navigation system and compensates for the navigation sensor error with a feedback structure through the optimization of the anomaly matching trajectory and dead reckoning trajectory. In addition, by determining the trajectory key-frame, the extended Kalman filter measurement update is performed using only the localization results with high accuracy. An open dataset was used to verify the performance of the algorithm, which was compared with existing algorithms. The proposed method is cost-effective, because the proposed method uses only an odometer, gyroscope, and magnetometer for indoor navigation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.