引入滑模观测器的GPS/INS组合导航滤波方法

由低成本器件组成的卫星/惯性(GPS/INS)组合导航系统中,存在较大的非线性与不确定性,为改善这一问题,本文提出一种引入滑模观测器(SMO)的滤波方法。首先,该方法建立了组合导航系统模型,介绍了扩展卡尔曼滤波(EKF)计算过程并分析存在的不足。然后,介绍了滑模观测器的基本原理,根据系统构建观测器。最后,说明了引入滑模观测器的EKF组合导航算法实现流程,滑模观测器将模型误差、状态估计以及均值方差融入EKF算法,修正系统输出。通过轨迹仿真实验与车载实验验证了所提方法优于传统EKF算法,具有更高的滤波精度。在车载实验中,卫星信号失锁15 s情况下,与EKF方法相比,所提方法的东向位置误差降低了53%,北向位置误差降低了37%,证明该方法能够有效抑制GPS/INS组合导航误差发散,为以后工程实践提供一定的参考价值。

GPS/INS integrated navigation filtering method based on sliding mode observer

There are great nonlinearity and uncertainty in the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation system composed of low cost devices. In order to improve this issue, a filtering method with Sliding Mode Observer (SMO) introduced is proposed in this paper. Firstly, in this method, the integrated navigation system model is established, the calculation process of Extended Kalman Filter (EKF) is introduced and its shortcomings are analysed. Then, the basic principle of SMO is introduced, and the Sliding Mode Observer is constructed according to the system. Finally, the implementation procedure of the EKF integrated navigation algorithm with SMO is explained. The SMO integrates the model error, state estimation and mean variance into EKF algorithm to correct the output of the system. The trajectory simulation experiment and vehicle test prove that the proposed method is superior to the traditional EKF method, and has higher filtering accuracy. In the vehicle test, when the satellite signal is out of lock for 15 s, compared with those of the EKF method, the eastbound and northbound position errors are reduced by 53%, 37%, respectively. The result proves that the proposed method can effectively suppress the error divergence of GPS/INS integrated navigation, and provides certain reference value for future engineering practice.