基于灰色马尔可夫预测的组合导航方法

针对传统MEMS/GNSS组合导航在卫星信号差时长时间精准导航问题,提出了基于灰色马尔可夫预测的MEMS/GNSS组合导航方法。通过改进灰色预测,增加马尔可夫修正环节,预测当卫星信号差时的GNSS量测值,进而代替原量测值,并将结果进行抗差扩展卡尔曼滤波(EKF),克服噪声干扰影响,提高了系统的稳定性。经仿真和跑车实验验证,该组合导航方法在卫星信号差时仍能输出较高精度的导航结果,且可以较好地克服异常观测值对系统的影响。

Integrated Navigation Method Based on Grey Markov Predictions

Aiming at the problem of long-term precision navigation of traditional MEMS/GNSS integrated navigation in the case of bad satellite signal, a MEMS/GNSS integrated navigation method based on gray Markov prediction is proposed in this paper. The stability of the system is improved by improving the gray prediction, adding Markov correction links, predicting the GNSS measurement value when the satellite signal is poor, and then replacing the original measurement value, and the result is subject to robust extended Kalman filtering(EKF) to overcome the influence of noise interference. The simulation and sports car experiments verify that the integrated navigation method can still output high-precision navigation results when the satellite signal is poor, and can better overcome the influence of abnormal observations on the system.