一种改进的无迹Kalman滤波在SINS/GPS组合导航中的应用
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摘要
传统的无迹Kalman滤波根据估计量测方程和量测量的协方差矩阵来确定最佳增益,但在导航过程中会因为外界因素的干扰,无法得到准确的量测信息算法,使增益有所偏差,导致最后的滤波精度降低。基于此,提出了一种改进的无迹Kalman滤波。首先根据新息,判断是否有异常的观测量,并通过引入抗差因子进行修正。通过仿真实验,比较扩展Kalman滤波、无迹Kalman滤波和改进的无迹Kalman滤波的误差特性,证明提出算法的有效性。
The traditional unscented Kalman filter(UKF)determines the optimal gain based on the estimated measurement equation and the covariance matrix of the quantity measurement.However,in the navigation process,due to the interference of external factors,accurate measurement information cannot be obtained,resulting in gain.The deviation causes the final filtering accuracy to decrease.Based on this,an improved unscented Kalman filter is proposed in this paper.Firstly,based on the new interest,it is judged whether there is an abnormal observation,and this is corrected by introducing a resistance factor.Through the simulation experiments,the error characteristics of extended Kalman filter(EKF),unscented Kalman filter and improved unscented Kalman filter are compared,and the effectiveness of the proposed algorithm is proved.
The traditional unscented Kalman filter(UKF)determines the optimal gain based on the estimated measurement equation and the covariance matrix of the quantity measurement.However,in the navigation process,due to the interference of external factors,accurate measurement information cannot be obtained,resulting in gain.The deviation causes the final filtering accuracy to decrease.Based on this,an improved unscented Kalman filter is proposed in this paper.Firstly,based on the new interest,it is judged whether there is an abnormal observation,and this is corrected by introducing a resistance factor.Through the simulation experiments,the error characteristics of extended Kalman filter(EKF),unscented Kalman filter and improved unscented Kalman filter are compared,and the effectiveness of the proposed algorithm is proved.
引文
[1] Luo J,Gang Y,Tang L J.Attitude estimation of quad-rotor aircraft based on hybrid filter[J].Measurement&Control Technology,2017,36(7):8-12.
[2] Kim J,Lee S.A vehicular positioning with GPS/IMU using adaptive control of filter noise covariance[J].ICT Express,2016,2(1):41-46.
[3] Tong X,Li Z F,Han G W,et al.Adaptive EKF based on HMM recognizer for attitude estimation using MEMS MARG sensors[J].IEEE Sensors Journal,2017(99):1.
[4]苗岳旺,周巍,田亮,等.基于新息χ~2检测的扩展抗差Kalman滤波及其应用[J].武汉大学学报(信息科学版),2016,41(2):269-273.
[5] Chang L,Li J,Chen S.Initial alignment by attitude estimation for strapdown inertial n-avigation systems[J].IEEE Transactions on In-strumentation&Measurment,2015,64(3):784-794.
[6]张靖,陈鸿跃,陈雨,等.一种基于联邦Kalman滤波器的多源信息融合定位算法[J].导弹与航天运载技术,2018,4(2):90-98.
[7]丰泽斌,马龙华,朱国杰.导航定位过程中的一种高效卫星组合选取策略[J].航天控制,2018,3(3):15-20.
[8]鲍水达,张安,毕文豪.快速强跟踪UKF算法及其在机动目标跟踪中的应用[J].系统工程与电子技术,2018,4(6):1189-1196.
[9]叶晨,崔双喜.一种强跟踪UKF及其在GPS/SINS深组合导航中的应用[J].导弹与航天运载技术,2018,4(2):61-64.
[10]付心如,徐爱功,孙伟.抗差自适应UFK的INS/GNSS组合导航算法[J].导航定位学报,2017,5(2):111-116.
[11]杨元喜.自适应动态导航定位[M].北京测绘出版社,2006:99-100.
[2] Kim J,Lee S.A vehicular positioning with GPS/IMU using adaptive control of filter noise covariance[J].ICT Express,2016,2(1):41-46.
[3] Tong X,Li Z F,Han G W,et al.Adaptive EKF based on HMM recognizer for attitude estimation using MEMS MARG sensors[J].IEEE Sensors Journal,2017(99):1.
[4]苗岳旺,周巍,田亮,等.基于新息χ~2检测的扩展抗差Kalman滤波及其应用[J].武汉大学学报(信息科学版),2016,41(2):269-273.
[5] Chang L,Li J,Chen S.Initial alignment by attitude estimation for strapdown inertial n-avigation systems[J].IEEE Transactions on In-strumentation&Measurment,2015,64(3):784-794.
[6]张靖,陈鸿跃,陈雨,等.一种基于联邦Kalman滤波器的多源信息融合定位算法[J].导弹与航天运载技术,2018,4(2):90-98.
[7]丰泽斌,马龙华,朱国杰.导航定位过程中的一种高效卫星组合选取策略[J].航天控制,2018,3(3):15-20.
[8]鲍水达,张安,毕文豪.快速强跟踪UKF算法及其在机动目标跟踪中的应用[J].系统工程与电子技术,2018,4(6):1189-1196.
[9]叶晨,崔双喜.一种强跟踪UKF及其在GPS/SINS深组合导航中的应用[J].导弹与航天运载技术,2018,4(2):61-64.
[10]付心如,徐爱功,孙伟.抗差自适应UFK的INS/GNSS组合导航算法[J].导航定位学报,2017,5(2):111-116.
[11]杨元喜.自适应动态导航定位[M].北京测绘出版社,2006:99-100.