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首页> 外文期刊>Journal of navigation >Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system
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Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

机译：基于幂速导航系统/多普勒速度日志集成系统的基于基于指数加权移动平均的测量噪声估计

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摘要

The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.
机译：具有集成多普勒速度日志（DVL）的绞线惯性导航系统（SINS）广泛用于水下导航。然而，在复杂的水下环境中，DVL信息可能已损坏，因此由于SINS / DVL集成系统中的卡尔曼滤波器的精度降低了。为了解决这一点，通常应用具有测量噪声估计器以提供噪声统计特征的自适应卡尔曼滤波器（AKF）。然而，类似于移动窗口（MW）和指数加权移动平均（EWMA）的现有方法不能适应动态环境，这导致噪声估计性能不令人满意。此外，必须凭经验确定遗忘因子。因此，本文提出了一种改进的EWMA（IEWMA）方法，具有用于测量噪声估计的自适应遗忘因子。首先，建立了SINS / DVL集成系统的模型，然后示出了MW和EWMA的测量噪声估计。随后，介绍了在没有经验的情况下对各种环境进行适应性的所提出的IEWMA方法。最后，进行了模拟和车辆测试以评估所提出的方法的有效性。结果表明，该方法在测量噪声估计和导航精度方面优于MW和EWMA方法。

著录项

  • 来源
    《Journal of navigation》 |2021年第2期|467-487|共21页
  • 作者

    Hou Lanhua; Xu Xiaosu; Yao Yiqing; Wang Di; Tong Jinwu;

  • 作者单位

    Southeast Univ Sch Instrument Sci & Engn Minist Educ Key Lab Microinertial Instrument & Adv & Ation Te Nanjing Peoples R China;

    Southeast Univ Sch Instrument Sci & Engn Minist Educ Key Lab Microinertial Instrument & Adv & Ation Te Nanjing Peoples R China;

    Southeast Univ Sch Instrument Sci & Engn Minist Educ Key Lab Microinertial Instrument & Adv & Ation Te Nanjing Peoples R China;

    Southeast Univ Sch Instrument Sci & Engn Minist Educ Key Lab Microinertial Instrument & Adv & Ation Te Nanjing Peoples R China;

    Southeast Univ Sch Instrument Sci & Engn Minist Educ Key Lab Microinertial Instrument & Adv & Ation Te Nanjing Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    measurement noise estimation; forgetting factor; adaptive Kalman filter; SINS; DVL;

    机译：测量噪声估计;遗忘因子;自适应卡尔曼滤波器;罪;DVL;

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