智能Kalman滤波在水下地形组合导航系统中的应用  

Application of intelligent Kalman filter to under-water terrain integrated navigation system

作　　者：李佩娟[1] 徐晓苏[1] 张小飞[2] 

机构地区：[1]东南大学微惯性仪表与先进导航技术教育部重点实验室,南京210096 [2]国网电力科学研究院,南京210003

出　　处：《中国惯性技术学报》2011年第5期579-583,589,共6页Journal of Chinese Inertial Technology

基　　金：国家自然科学基金项目(60874092);国家自然科学基金项目(50575042)

摘　　要：为了避免现实环境的动态变化对水下航行器系统模型造成的随机干扰影响,保证水下航行器长时间导航精度的稳定性,提出了利用RBF神经网络辅助联邦Kalman滤波方法对SINS/TAN/DVL/MCP组合导航系统进行信息融合。给出了各子导航系统的误差模型,通过足够精度的样本对前向神经网络进行离线训练,建立神经网络控制模型。仿真结果表明,该方法可使水下航行器的系统状态在较短的时间内以较高的精度达到稳定。通过与联邦Kalman滤波结果对比表明,采用智能控制方法辅助的信息融合方式的导航定位精度提高了一倍,能有效提高常规联邦Kalman滤波器的自适应能力,达到减小误差,提高精度的目的。In order to avoid the random effects of the underwater vehicle system model caused by the real dynamics of the environment,a RBF neural network aided federated Kalman filter is proposed to fuse the navigation information from various navigation sensors（such as SINS,TAN,DVL and MCP） to improve the navigation accuracy.Error models of navigation systems are proposed and a neural network model is set up via offline training with enough precise samples.Simulation experiments prove that the AUV navigation accuracy can attain high stability in a short period by the proposed method.By comparing the filtering results with the federated Kalman filter,it is shown that the proposed method can effectively improve the adaptivity,and the navigation precision is doubled.

关 键 词：组合导航系统 地形匹配 RBF神经网络 联邦卡尔曼滤波 

分 类 号：U666.1[交通运输工程—船舶及航道工程]