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A real-time prognostic method for the drift errors in the inertial navigation system by a nonlinear random-coefficient regression model

机译：非线性随机系数回归模型的惯性导航系统漂移误差实时预测方法

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摘要

Inertial navigation systems have been widely used in both civilian and military systems because of their autonomous navigation capability. Nevertheless, due to its autonomous characteristics, the navigation precision of an inertial navigation system is heavily influenced by its drift errors, which results from the performance degradation of the system in use. One of the most effective means of eliminating such adverse effects is to predict the drift error values in advance, and compensate for them subsequently. It is therefore significantly important to accurately predict the degrading trend of the drift errors of an inertial navigation system. We propose a novel degradation modeling method based on a nonlinear random-coefficient regression model to predict the drift errors. The parameters of the model are dynamically updated by the expectation maximization algorithm, in conjunction with the Bayesian inference method at the time when a new drift error data is observed. In doing this, the degrading trend of the drift errors can be predicted in real time. Finally, a batch of drift error data of an inertial navigation system is used to validate the feasibility and effectiveness of the developed prognostic method.
机译：惯性导航系统由于其自主导航能力而已广泛用于民用和军事系统。然而，由于惯性导航系统的自主特性，惯性导航系统的导航精度受其漂移误差的影响很大，而漂移误差是由于使用中的系统性能下降而引起的。消除此类不利影响的最有效方法之一是预先预测漂移误差值，然后对其进行补偿。因此，准确地预测惯性导航系统的漂移误差的下降趋势非常重要。我们提出了一种基于非线性随机系数回归模型的新的退化建模方法，以预测漂移误差。在观察到新的漂移误差数据时，通过期望最大化算法结合贝叶斯推断方法，动态更新模型的参数。这样，可以实时预测漂移误差的下降趋势。最后，使用惯性导航系统的一批漂移误差数据来验证所开发的预测方法的可行性和有效性。

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  • 来源
    《Acta astronautica》 |2014年第octaanova期|45-54|共10页
  • 作者

    Zhaoqiang Wang; Wenbin Wang; Changhua Hu; Xiaosheng Si; Juan Li;

  • 作者单位

    Department of Automation, Xi'an Institute of High-Tech, Xi'an, Shaanxi 710025, PR China,Dongling School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, PR China;

    Dongling School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, PR China;

    Department of Automation, Xi'an Institute of High-Tech, Xi'an, Shaanxi 710025, PR China;

    Department of Automation, Xi'an Institute of High-Tech, Xi'an, Shaanxi 710025, PR China;

    College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao, Shandong 266109, PR China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Nonlinear degradation model; Random-coefficient regression; Expectation maximization; Inertial navigation system; Real-time prognosis;

    机译：非线性退化模型;随机系数回归;期望最大化;惯性导航系统;实时预后;

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