摘要
针对城市环境下的全球卫星导航系统(GNSS)信号异常导致的车辆组合定位系统定位精度下降的问题,提出了一种基于模糊自适应卡尔曼滤波的车辆组合定位算法。利用模糊推理系统监测GNSS提供的辅助信息并输出量测噪声调整系数,利用改进的Sage-Husa自适应滤波算法对量测噪声进行自适应估计,并与误差状态卡尔曼滤波(ESKF)相结合,通过及时调整量测噪声协方差的方式提高系统在GNSS信号异常区域的定位精度,同时加入车辆运动约束对模型进行修正。采用计算机仿真和实车实验的方式对所提算法的性能进行了验证,结果表明:所提算法能够及时有效地对量测噪声进行调整,在GNSS信号异常区域的定位精度相较于标准的ESKF算法有明显提升。
In recent years, with the rapid development of intelligent driving technology, the requirements of vehicles for environmental perception, positioning, decision-making, control and other systems are increasing. As a prerequisite and basis for other systems, real-time and accurate positioning of a vehicle has important research values. The Global Navigation Satellite System(GNSS) can provide absolute positioning results without accumulative errors, but it has weak resistance to interference and low output frequency. Inertial Navigation System(INS), on the other hand, has a high sampling frequency and good real-time performance, but cannot be used alone for long periods of time due to the accumulative errors. As the combination of the two can effectively complement each other, the GNSS/INS integrated positioning algorithm is widely used as a mainstream positioning method in the field of vehicle navigation and positioning.However, as the main working condition of a vehicle, an urban environment is complex, causing challenges for vehicle positioning systems. Tall buildings, trees and overpasses in an urban environment can block satellite signals, resulting in a significant reduction in GNSS positioning accuracy. When using the Kalman filter to fuse GNSS and INS measurements, a vehicle may receive anomalous GNSS positioning results, which can lead to reduced performance of the integrated vehicle positioning system and cause driving safety problems if the filter parameters are not adjusted in a timely and accurate way.Aiming at the problem of a reduced positioning accuracy of the integrated vehicle positioning system due to GNSS signal anomalies in urban environments, this paper proposes a fuzzy adaptive Kalman filter-based integrated vehicle positioning algorithm. The algorithm makes full use of the auxiliary information provided by GNSS(the number of visible satellites and position dilution of precision), builds a fuzzy inference system to monitor them and outputs the adjustment coefficient of measurement noise. The improved Sage-Husa algorithm is then used to further estimate the measurement noise based on the filter innovation. Combining the above methods, the covariance of the measurement noise in the filter parameters is adjusted timely and accurately by fuzzy adaptive estimation. Using the vehicle position error, velocity error, orientation error and the error in the bias of the accelerometer and gyroscope in three axis as state vectors, the Error State Kalman Filter(ESKF) is used to fuse the GNSS and INS measurements by replacing the fixed filter parameters of the standard ESKF with the measurement noise covariance obtained by the fuzzy adaptive estimation algorithm described above. At the same time, with reference to the kinematic characteristics of the ground vehicle, the kinematic constraints of the vehicle are constructed to further correct the errors of the GNSS/INS integrated positioning system by means of measurement updates. The performance of the proposed algorithm is verified by computer simulation and real vehicle experiments. The results show that the proposed fuzzy adaptive Kalman filter-based integrated vehicle positioning algorithm can adjust the measurement noise covariance in a timely and accurate way and correct the positioning error by vehicle kinematic constraints, and it can still provide accurate positioning results in areas where GNSS signals are anomalous. The positioning accuracy of the proposed algorithm is significantly improved compared to the standard ESKF algorithm.
作者
朱元
吴博宇
陆科
吴名芝
ZHU Yuan;WU Boyu;LU Ke;WU Mingzhi(School of Automotive Studies,Tongji University,Shanghai 201800,China;Nanchang Automotive Institute of Intelligence and New Energy,Nanchang 330052,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2023年第1期9-18,共10页
Journal of Chongqing University of Technology：Natural Science
基金
南昌智能新能源汽车研究院前瞻课题项目(TPD-TC202110-13)。
