Abstract The integrated navigation system based on multi-sensor data fusion could effectively improve the navigation accuracy for Autonomous Underwater Vehicle (AUV). Various navigation equipment and sensors have different error characteristics. Current research does not take a specific processor based on their error characteristics, causing the optimal estimation is hard to realize in engineering applications. To address this issue, this study presents a robust and practical integrated navigation algorithm. When the AUV works on the surface where the Global Position System (GPS) could obtain the position, the navigation system employs an adaptive fault-tolerance filter to smooth the GPS trajectory, then the processed GPS information would be used to correct the Inertial Navigation System (INS). Otherwise, Variational Bayesian (VB) is adopted to estimate the measurement error covariance of the Doppler Velocity Log (DVL), which would be used for the INS/DVL integration system. Subsequently, the pressure sensor (PS) uses the conventional method to correct the height error of INS. The above information would be fused to obtain the position when the AUV operates underwater. The real experiment data of our independently developed Sailfish AUV is processed to evaluate the algorithm performance. Experimental results illustrate that the proposed algorithm could improve the navigation accuracy and the robustness of resisting unknown measurement uncertainty compared to the conventional method.

中文翻译：

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一种实用的INS/GPS/DVL/PS组合导航算法及其在自主水下航行器上的应用

摘要 基于多传感器数据融合的组合导航系统可以有效提高自主水下航行器（AUV）的导航精度。各种导航设备和传感器具有不同的误差特性。目前的研究并没有根据其误差特性选取特定的处理器，导致工程应用中难以实现最优估计。为了解决这个问题，本研究提出了一种鲁棒且实用的集成导航算法。当AUV工作在全球定位系统（GPS）可以获取位置的表面时，导航系统采用自适应容错滤波器来平滑GPS轨迹，然后处理后的GPS信息将用于校正惯性导航系统(INS)。除此以外，采用变分贝叶斯 (VB) 估计多普勒速度测井 (DVL) 的测量误差协方差，用于 INS/DVL 集成系统。随后，压力传感器（PS）采用常规方法修正INS的高度误差。当AUV在水下运行时，上述信息将被融合以获得位置。对我们自主研发的Sailfish AUV的真实实验数据进行处理，评估算法性能。实验结果表明，与传统方法相比，所提出的算法能够提高导航精度和抵抗未知测量不确定性的鲁棒性。压力传感器（PS）采用常规方法修正INS的高度误差。当AUV在水下运行时，上述信息将被融合以获得位置。对我们自主研发的Sailfish AUV的真实实验数据进行处理，评估算法性能。实验结果表明，与传统方法相比，所提出的算法能够提高导航精度和抵抗未知测量不确定性的鲁棒性。压力传感器（PS）采用常规方法修正INS的高度误差。当AUV在水下运行时，上述信息将被融合以获得位置。对我们自主研发的Sailfish AUV的真实实验数据进行处理，评估算法性能。实验结果表明，与传统方法相比，所提出的算法能够提高导航精度和抵抗未知测量不确定性的鲁棒性。