论文标题
混合自适应速度辅助导航过滤器，并应用于INS/DVL融合
A Hybrid Adaptive Velocity Aided Navigation Filter with Application to INS/DVL Fusion
论文作者
论文摘要
自动水下车辆（AUV）通常在许多水下应用中使用。通常，在非线性过滤器中使用惯性传感器和多普勒速度对数读数来估计AUV导航解决方案。过程噪声协方差矩阵根据惯性传感器的特性调整。该矩阵极大地影响了滤波器的准确性，鲁棒性和性能。一种常见的做法是假设此矩阵在AUV操作过程中是固定的。但是，随着时间的流逝，它随着时间的流逝而变化，因为不确定性尚不清楚。因此，该矩阵的自适应调整可以导致过滤性能的显着改善。在这项工作中，我们提出了一个基于学习的自适应速度辅助导航过滤器。为此，生成手工制作的功能并用于调整瞬时系统噪声协方差矩阵。一旦学习了过程噪声协方差，就可以将其输入基于模型的导航过滤器中。模拟结果表明，与其他自适应方法相比，我们的方法的好处。
Autonomous underwater vehicles (AUV) are commonly used in many underwater applications. Usually, inertial sensors and Doppler velocity log readings are used in a nonlinear filter to estimate the AUV navigation solution. The process noise covariance matrix is tuned according to the inertial sensors' characteristics. This matrix greatly influences filter accuracy, robustness, and performance. A common practice is to assume that this matrix is fixed during the AUV operation. However, it varies over time as the amount of uncertainty is unknown. Therefore, adaptive tuning of this matrix can lead to a significant improvement in the filter performance. In this work, we propose a learning-based adaptive velocity-aided navigation filter. To that end, handcrafted features are generated and used to tune the momentary system noise covariance matrix. Once the process noise covariance is learned, it is fed into the model-based navigation filter. Simulation results show the benefits of our approach compared to other adaptive approaches.
