中文摘要：

为了提高自主水下航行器组合导航系统精度,选择了捷联式惯性导航系统、多普勒速度声纳、电子磁罗经和海底地形匹配作为水下航行器组合导航系统导航传感器,建立了水下航行器组合导航系统的状态模型和导航传感器观测模型,提出了一种基于RBF神经网络进行联邦滤波信息分配的自适应联邦滤波信息融合方法并进行了计算机软件仿真试验,仿真实验结果表明：采用RBF神经网络进行信息分配系数的自适应调整的改进自适应联邦滤波器的水下航行器组合导航系统的导航姿态、速度和位置精度得到了提高,满足高精度水下组合导航的要求。新型信息融合方法克服了传统滤波容易发散的缺点,有效地提高了水下航行器组合导航系统的容错性能和导航精度。

英文摘要：

To improve the AUV.（Autonomous Underwater Vehicle） navigation accuracy, SINS （Strapdown Inertial Navigation System）, DVS （Doppler Velocity Sonar）, electromagnetic compass and TAN （Terrain Aided Navigation） were adopted in the A UV integrated navigation system. The state transfer mathematic model of the AUV integrated navigation system and the observation model of the chosen navigation sensors were built according the system simulation experiments data. An improved federated filter based on RBF neural network for adjusting the information sharing factors was designed and implemented in the AUV integrated navigation system. Simulation experiments were carried out according to the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy was improved substantially with the specified sensors and novel federated filter and satisfied the requirements of precision navigation. The novel integrated navigation system is effective in prohibiting the divergence of the filter and improving fault tolerance ability and the navigation accuracy of the AUV integrated navigation system.