Song Rui, Chen Xiyuan, Fang Yongchun, Huang Haoqian
Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China.
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, Jiangsu, China.
ISA Trans. 2020 Oct;105:387-395. doi: 10.1016/j.isatra.2020.05.049. Epub 2020 Jun 2.
Information fusion of the GPS/INS integrated system is always related to characteristics of the inertial system and the sensor feature, yet prior knowledge is still difficult to obtain in real applications. To deal with the uncertainty of error covariance and state noise in vehicle navigation, this paper presents a novel approach, wherein the integration of Square-root Cubature Kalman Filters (SCKF) and Interacting Multiple Model (IMM) are also introduced. In the framework of IMM, the SCKFs with different covariance are designed to reflect various vehicle dynamics. Besides, since the IMM-SCKF can switch flexibly among the filters, the transition probability matrix is computed with maximum likelihood method to adapt to different noise characteristics. The performance of the proposed algorithm is guaranteed by theoretical analyses, and a series of vehicular experiments with different maneuvers are carried out in an urban environment. The results indicate that, in comparison with the CKF and the IMM-CKF, the accuracy of velocity and attitude are increased by the proposed strategy.
全球定位系统/惯性导航系统（GPS/INS）集成系统的信息融合总是与惯性系统的特性和传感器特征相关，但在实际应用中仍难以获得先验知识。为了处理车辆导航中误差协方差和状态噪声的不确定性，本文提出了一种新方法，其中还引入了平方根容积卡尔曼滤波器（SCKF）和交互式多模型（IMM）的集成。在IMM框架下，设计了具有不同协方差的SCKF来反映车辆的各种动力学特性。此外，由于IMM-SCKF可以在滤波器之间灵活切换，因此采用最大似然法计算转移概率矩阵以适应不同的噪声特性。通过理论分析保证了所提算法的性能，并在城市环境中进行了一系列不同机动的车辆实验。结果表明，与CKF和IMM-CKF相比，所提策略提高了速度和姿态的精度。
