School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China; Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education, Nanjing, Jiangsu 210096, China.
School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China; Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education, Nanjing, Jiangsu 210096, China.
ISA Trans. 2018 Jan;72:138-146. doi: 10.1016/j.isatra.2017.09.019. Epub 2017 Oct 10.
In order to improve the accuracy of GNSS/INS working in GNSS-denied environment, a robust cubature Kalman filter (RCKF) is developed by considering colored measurement noise and missing observations. First, an improved cubature Kalman filter (CKF) is derived by considering colored measurement noise, where the time-differencing approach is applied to yield new observations. Then, after analyzing the disadvantages of existing methods, the measurement augment in processing colored noise is translated into processing the uncertainties of CKF, and new sigma point update framework is utilized to account for the bounded model uncertainties. By reusing the diffused sigma points and approximation residual in the prediction stage of CKF, the RCKF is developed and its error performance is analyzed theoretically. Results of numerical experiment and field test reveal that RCKF is more robust than CKF and extended Kalman filter (EKF), and compared with EKF, the heading error of land vehicle is reduced by about 72.4%.
为了提高在 GNSS 拒止环境下 GNSS/INS 的工作精度，考虑有色测量噪声和观测丢失，开发了一种鲁棒容积卡尔曼滤波器（RCKF）。首先，通过考虑有色测量噪声推导出一种改进的容积卡尔曼滤波器（CKF），其中应用了时间差分方法来产生新的观测值。然后，在分析现有方法的缺点后，将处理有色噪声的测量增强转化为处理 CKF 的不确定性，并利用新的 sigma 点更新框架来考虑有界模型不确定性。通过在 CKF 的预测阶段重新使用扩散的 sigma 点和近似残差，开发了 RCKF，并从理论上分析了其误差性能。数值实验和现场测试的结果表明，RCKF 比 CKF 和扩展卡尔曼滤波器（EKF）更具鲁棒性，与 EKF 相比，陆地车辆的航向误差减少了约 72.4%。
