Abstract
In this paper, a fuzzy-innovation based adaptive extended Kalman filter (FI-AKF) is proposed to improve the performance of the GNSS/INS fusion system, which is degraded due to satellite signal cutoff and attenuation and inaccurate modeling in dense urban environments. The information used for sensor fusion is obtained from real-time kinematic (RTK), micro-electro-mechanical system based inertial measumrement unit (MEMS-IMU), and on-board diagnostics (OBD). The fuzzy logic system is proposed to adaptively update the measurement covariance matrix of the RTK according to the position dilution of precision (PDOP), the number of receivable satellites, and the innovation of the extended Kalman filter (EKF). In addition, the driving state of the vehicle is defined as stop, straight run, left/right turn, and the like. To reduce the heading estimation error of the Kalman filter, the estimated heading is corrected according to the driving state. Also, the measurement covariance matrices of IMU and OBD are applied adaptively considering the characteristics of each sensor according to the driving state. In order to analyze the performance of the proposed FI-AKF positioning system in a dense urban environment, a computer simulation is performed. The proposed FI-AKF is compared to the performance of the existing extended Kalman filter and the innovation-based adaptive extended Kalman filter. In addition, we conduct a performance comparison experiment with a commercial positioning system in the field test. Through each experiment, it is confirmed that the proposed FI-AKF system has higher positioning performance than the comparison positioning systems in a dense urban environment.
Highlights
IntroductionAccording to a standard document [1] released by the National Highway Traffic Safety
According to a standard document [1] released by the National Highway Traffic SafetyAdministration (NHTSA), a high accuracy of vehicle positioning is required to provide a seamless vehicle safety service
To be used in the positioning of vehicles running in the city, the adaptive Kalman filters based on the Innovation-Based Adaptive Estimation (IAE) have problems in that they do not accurately reflect a change in the driving state of the vehicle or the change of state in the place where the performance change of the satellite navigation position sensor is severe, such as urban environments
Summary
According to a standard document [1] released by the National Highway Traffic Safety. Zheng [20] proposed a robust adaptive unscented Kalman filter (RAUKF) to improve the accuracy of state estimation and robustness of UKF This method includes the use of an online fault-detection mechanism to judge whether the current noise covariance needs to be updated or not. To be used in the positioning of vehicles running in the city, the adaptive Kalman filters based on the IAE have problems in that they do not accurately reflect a change in the driving state of the vehicle or the change of state in the place where the performance change of the satellite navigation position sensor is severe, such as urban environments. In order to improve the vehicle positioning performance in a dense urban environment, it is necessary to design a sensor fusion filter that accurately models the sensor’s characteristics.
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