SINS/DVL/AST水下组合导航中的鲁棒信息融合方法

捷联式惯导系统由于自主性强等优势成为自主式水下航行器长航时、长航程导航的主要手段。针对水下环境中外部多导航传感器如多普勒计程仪提供的测速信息和水声单应答器提供的位置信息容易受到非高斯噪声污染的问题,提出基于马氏距离算法的联邦鲁棒卡尔曼滤波算法。在联邦鲁棒卡尔曼滤波算法中,通过马氏距离算法引入膨胀因子,对量测噪声协方差阵进行膨胀,以实现非高斯条件下水下组合导航系统鲁棒性的提升。同时基于子滤波器的滤波性能对信息分配系数进行自适应调整以确保水下组合导航系统的高精度。基于江试试验实测数据进行水下组合导航半物理仿真试验,试验结果表明:相比于传统的联邦卡尔曼滤波算法,联邦鲁棒卡尔曼滤波算法可在非高斯条件下实现更高精度、更加稳定的组合导航；能够满足水下组合导航系统对容错性和鲁棒性的要求。

Robust information fusion method in SINS/DVL/AST underwater integrated navigation

Strapdown inertial navigation system has been the main navigation and positioning method for long voyage and long-endurance underwater navigation. In order to solve the problem that velocity information provided by Doppler velocity log and position information provided by acoustic single transponder are easily contaminated by non-Gaussian noise, a federated robust Kalman filter algorithm was proposed. In the proposed method, the Mahalanobis distance algorithm was used to introduce a inflated factor to inflate the measurement noise covariance, which can improve the robustness of integrated navigation system. At the same time, the information distribution coefficient was adaptively tuned based on the performance of the sub-filter, which can guarantee the accuracy of integrated navigation system. The semi-physical simulation test for underwater integrated navigation was carried out by the federated robust Kalman filter algorithm and traditional federated Kalman filter algorithm based on measured data of the river test. The experiment results demonstrate that the federated robust Kalman filter algorithm has better performance in underwater integrated navigation compared with the traditional federated Kalman filter algorithm under the non-Gaussian condition and it can meet the requirements of fault tolerance and robustness for underwater integrated navigation system.