水下重力梯度辅助惯性导航匹配算法研究

项目名称： 水下重力梯度辅助惯性导航匹配算法研究

项目编号： No.41274029

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 天文学、地球科学

项目作者： 李姗姗

作者单位： 中国人民解放军信息工程大学

项目金额： 70万元

中文摘要： 重力梯度辅助惯性导航系统具有自主性强、隐蔽性好、不受时间限制及定位精度高等诸多优点，将其付诸于实际需攻克的关键技术之一是重力梯度匹配算法。课题针对该系统中重力参数与状态参数之间的非线性关系，以及系统受非高斯型噪声影响等问题，重点研究非线性卡尔曼滤波重力梯度匹配算法。主要内容包括：基于相关先验信息深入分析海洋重力场空间属性与频谱特征，探索惯导与重力梯度仪测量误差的精确建模方法，由此建立滤波状态方程与量测方程；研究构建EKF、UKF、PF匹配算法模型，在不同重力梯度特征区进行实验验证，从滤波效果、滤波精度、滤波运行时间等方面评估导航性能，并综合各类算法优缺点，建立最优融合匹配算法模型；在满足潜器自主导航精度的前提下，研究提出重力梯度匹配导航对重力梯度仪测量精度、重力基准图格网分辨率与精度、局部重力场统计特征变化范围的应用需求。课题成果可为真实海洋环境潜器的精确自主导航提供理论与技术支撑。

中文关键词： 重力梯度辅助惯性导航；匹配算法；扩展卡尔曼滤波；并行无味卡尔曼滤波；匹配概率

英文摘要： Gravity gradient aided inertial navigation system has a variety of advantages such as good autonomy, well covert, anytime availability and high positioning precision, of which one key technique is gravity gradient matching algorithm when applied into reality. Aiming at the nonlinear relationship between gravity parameters and state parameters and the impact of non-Gaussian noise on the system, nonlinear Kalman filtering matching algorithms of gravity gradient are important problems in study. The project covers the following three issues. Firstly, the further analysis of spatial attributes and spectrum characteristics of the oceanic gravity field with related a priori information, the construction of precise modeling methods of measurement errors of inertial navigation and gravity gradiometer instrument, and the establishment of filtering state equation and measurment equation. Secondly, the research of matching algorithm models based on filters like EKF, UKF and PF, the experiments in some regions with various statistical characteristics of gravity gradient, the evaluation of navigation performance in terms of effect, accuracy and time cosumption of filtering, the building of an optimal fusing matching algorithm model based on the advantages and disadvantages. Finally, on the precondition of satisfying the navig

英文关键词： gravity gradient aided inertial navigation system；matching algorithm；Extended Kalman Filtering(EKF)；Parallel Unscented Kalman Filtering(PUKF)；matching probability