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Error Prediction for SINS/GPS after GPS Outage Based on Hybrid KF-UKF

机译：基于混合KF-UKF的GPS停电后SINS / GPS的误差预测

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摘要

The performance of MEMS-SINS/GPS integrated system degrades evidently during GPS outage due to the poor error characteristics of low-cost IMU sensors. The normal EKF is unable to estimate SINS error accurately after GPS outage owing to the large nonlinear error caused by MEMS-IMU. Aiming to solve this problem, a hybrid KF-UKF algorithm for real-time SINS/GPS integration is presented in this paper. The linear and nonlinear SINS error models are discussed, respectively. When GPS works well, we fuse SINS and GPS with KF with linear SINS error model using normal EKF. In the case of GPS outage, we implement Unscented Transform to predict SINS error covariance with nonlinear SINS error model until GPS signal recovers. In the simulation test that we designed, an evident accuracy improvement in attitude and velocity could be noticed compared to the normal EKF method after the GPS signal recovered.
机译：由于低成本IMU传感器的差错特性，MEMS-SINS / GPS集成系统的性能在GPS中断期间会明显下降。由于MEMS-IMU引起的较大的非线性误差，正常的EKF无法在GPS中断后准确估计SINS误差。为了解决这个问题，本文提出了一种用于实时SINS / GPS集成的混合KF-UKF算法。分别讨论了线性和非线性SINS误差模型。当GPS正常工作时，我们使用正常的EKF将SINS和GPS与KF融合在一起，并具有线性SINS误差模型。在GPS中断的情况下，我们实施无味变换来预测SINS误差与非线性SINS误差模型的协方差，直到GPS信号恢复为止。在我们设计的仿真测试中，在恢复GPS信号后，与常规的EKF方法相比，可以观察到姿态和速度的精度明显提高。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第18期|239426.1-239426.9|共9页
  • 作者

    Zhang Baiqiang; Chu Hairong; Sun Tingting; Jia Hongguang; Guo Lihong; Zhang Yue;

  • 作者单位

    Univ Chinese Acad Sci, Beijing 10039, Peoples R China.;

    Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, Peoples R China.;

    Univ Chinese Acad Sci, Beijing 10039, Peoples R China.;

    Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, Peoples R China.;

    Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, Peoples R China.;

    Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, Peoples R China.;

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