In this paper, we propose a sea current relative navigation method using an interacting multiple model (IMM) filter with adaptive fading technique that can compensate an inaccurate sea current dynamics model. Due to the marine environment, the underwater vehicles largely depend on inertial navigation. Unfortunately, since its performance deteriorates with time, it is usually aided by another sensor. An electromagnetic-log (EM-log) and a Doppler velocity log (DVL), which are mainly used in marine navigation, provide relative velocity measurements to the sea currents, and hence require an accurate sea current dynamics model to fully utilise them. However, it is difficult to reflect the actual sea current changes with just a single fixed model, resulting in degraded overall navigation performance. Therefore, this paper proposes an IMM filter that can use multiple sea current dynamics models and has sub-filters designed with adaptive fading extended Kalman filter (AFEKF) to compensate for the mismodelling of sea current dynamics. The method is verified by simulation and shows a performance improvement comparable to the optimal filter.

在本文中，我们提出了一种使用具有自适应衰落技术的交互多模型 (IMM) 滤波器的海流相对导航方法，可以补偿不准确的海流动力学模型。由于海洋环境的原因，水下航行器在很大程度上依赖于惯性导航。不幸的是，由于其性能会随着时间的推移而退化，因此通常需要另一个传感器来辅助。主要用于海洋导航的电磁测井仪（EM-log）和多普勒速度测井仪（DVL）提供海流的相对速度测量，因此需要精确的海流动力学模型才能充分利用它们。然而，单一的固定模型难以反映实际海流变化，导致整体导航性能下降。所以，本文提出了一种 IMM 滤波器，它可以使用多个海流动力学模型，并具有采用自适应衰落扩展卡尔曼滤波器 (AFEKF) 设计的子滤波器，以补偿海流动力学的错误建模。该方法通过仿真验证，并显示出与最优滤波器相当的性能改进。