Autonomous underwater vehicle (AUV) has been employed in oceanography applications based on a reliable navigation. The complex underwater environment leads to more velocity measurement errors of AUV, so it is difficult to determine the accurate navigation and positioning information. To solve the problem, a novel variational Bayesian-based filter for inaccurate input (VBFII) is proposed to determine the state information under the complex marine condition of inaccurate input. Firstly, the velocities are assumed to follow the Gaussian distribution, which can better describe the model of input information. Secondly, the augmentation method is used to augment the state vector and error covariance matrix to simplify calculation. The augmented state vector, the augmented predicted error covariance and measurement error noise matrices are derived more accurately based on the variational Bayesian (VB) approach. The experiment results show that the proposed VBFII has better estimation accuracy and robustness than other comparison algorithms.

中文翻译：

---

基于变分贝叶斯的滤波器解决水下导航中的不准确输入


自主水下航行器（AUV）已在基于可靠导航的海洋学应用中得到应用。复杂的水下环境导致AUV测速误差较多，难以确定准确的导航定位信息。为了解决该问题，提出了一种新颖的基于变分贝叶斯的不准确输入滤波器（VBFII）来确定不准确输入的复杂海洋条件下的状态信息。首先，假设速度服从高斯分布，这样可以更好地描述输入信息的模型。其次，采用增广方法对状态向量和误差协方差矩阵进行增广，以简化计算。基于变分贝叶斯（VB）方法更准确地导出增强状态向量、增强预测误差协方差和测量误差噪声矩阵。实验结果表明，所提出的VBFII比其他对比算法具有更好的估计精度和鲁棒性。