为满足自主式水下航行器（AUV）应对水下复杂环境和水下高精度导航的需求，提出了捷联式惯性导航系统（SINS）、多普勒计程仪（DVL）、磁罗经导航系统（MCP）和地形辅助导航系统（TAN）的水下组合导航系统方案，设计了一种基于BP神经网络的自适应滤波算法，建立了卡尔曼滤波器的线性滤波方程和导航传感器的量测方程，根据数学模型进行仿真实验。仿真结果表明，导航传感器和基于神经网络的自适应滤波技术的应用大大提高了AUV的导航精度和自适应能力。
This paper presents an integrated navigation system for underwater vehicles to adapt the characteristics of the underwater environment and high accuracy requirements of the underwater navigation, which is composed of the strapdown inertial navigation system (SINS), the Doppler velocity log (DVL) and the magnetic compass (MCP), Terrain-aided navigation system (TAN). An adaptive Kalman filter based on BP neural network is designed and implemented in the Autonomous Underwater Vehicle (AUV) integrated navigation system. Linear filter equations for the Kalman filter and measurement equations of navigation sensors are addressed. Simulation experiments are carried out according to the mathematical model. The results indicate that the AUV navigation precision and adaptive capacity are improved substantially with the proposed sensors and the intelligent Kalman filter.
2018,40(5): 117-122 收稿日期：2017-12-12
DOI：10.3404/j.issn.1672-7649.2018.05.022
分类号：U666.1
作者简介：查月(1977-),女,硕士,高级工程师,主要从事导航试验及靶标总体技术研究
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