In order to suppress the impact of some error factors of the existing strap-down inertial navigation system and ultra-short base line (SINS/USBL) position matching loosely integration approach on positioning accuracy, a tightly integration strategy is creatively proposed and designed relying on the derived state error equation and measurement equation, the relative measurement information are directly used as the observation information. The error factors such as attitude error and installation error are considered in the filtering model to avoid the influences on the matching position, and the filter algorithm can be also effectively designed according to the sensor parameters of USBL, which can improve the integration positioning accuracy. Meanwhile, in order to address the decreased positioning performance caused by the measurement uncertainty, a Student's t-based Kalman filter with adaptiveness and robustness is reorganized and derived for the proposed SINS/USBL tightly integration strategy, the adaptiveness can be obtained by estimating the unknown measurement noise statistics using variational Bayesian (VB) approximation, and the robustness can be achieved by dealing with the measurement outliers based on the Student's t distribution. Finally, the feasibility and the superiority of the proposed strategy are evidenced both through the simulations and the field tests in the river.

中文翻译：

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用于 SINS/USBL 紧密集成导航系统的学生基于 T 的测量不确定度滤波器

为抑制现有捷联惯导系统和超短基线（SINS/USBL）位置匹配松散集成方法的一些误差因素对定位精度的影响，创造性地提出并设计了一种紧集成策略。导出的状态误差方程和测量方程，相对测量信息直接作为观测信息。滤波模型中考虑了姿态误差、安装误差等误差因素，避免了对匹配位置的影响，同时还可以根据USBL的传感器参数有效设计滤波算法，提高积分定位精度。同时，为了解决测量不确定性导致的定位性能下降问题，针对所提出的 SINS/USBL 紧密集成策略，重新组织并导出了具有自适应性和鲁棒性的基于 Student t 的卡尔曼滤波器，可以通过使用变分贝叶斯 (VB) 近似估计未知测量噪声统计来获得自适应性，并且鲁棒性可以为通过处理基于学生 t 分布的测量异常值来实现。最后，通过模拟和河流现场试验证明了所提出策略的可行性和优越性。稳健性可以通过处理基于学生 t 分布的测量异常值来实现。最后，通过模拟和河流现场试验证明了所提出策略的可行性和优越性。稳健性可以通过处理基于学生 t 分布的测量异常值来实现。最后，通过模拟和河流现场试验证明了所提出策略的可行性和优越性。