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.

中文翻译：

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捷联惯导系统/多普勒速度测井综合系统基于改进指数加权移动平均的测量噪声估计

集成多普勒速度测井（DVL）的捷联惯性导航系统（SINS）广泛应用于水下导航。然而，在复杂的水下环境中，DVL信息可能会被破坏，从而导致SINS/DVL集成系统中卡尔曼滤波器的精度下降。为了解决这个问题，通常应用带有测量噪声估计器的自适应卡尔曼滤波器 (AKF)，以提供噪声统计特性。然而，现有的移动窗口（MW）和指数加权移动平均（EWMA）等方法无法适应动态环境，导致噪声估计性能不理想。此外，遗忘因子必须根据经验确定。因此，本文提出了一种改进的具有自适应遗忘因子的EWMA（IEWMA）方法用于测量噪声估计。首先，建立了 SINS/DVL 集成系统的模型，然后说明了基于 MW 和 EWMA 的测量噪声估计器。随后，介绍了所提出的在没有经验的情况下适应各种环境的IEWMA方法。最后，通过仿真和车辆测试来评估所提出方法的有效性。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。进行了仿真和车辆测试以评估所提出方法的有效性。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。进行了仿真和车辆测试以评估所提出方法的有效性。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。