摘要
重力匹配导航的定位精度高度依赖于所航行区域的重力场分布,因此,选择合适的导航适配区对于确保重力匹配导航性能较为关键。为了确保重力匹配导航具有较高的定位精度,提出了一种基于重力场深层感知模型的重力匹配导航适配区选取算法,旨在提高重力匹配导航适配区选取的准确性。首先,使用灰度共生矩阵、局部二值模式和提出的归一化重力尺度,构建重力场特征集;然后,设计重力场深层感知模型,提出了层间传播机制用于训练该模型;最后,将重力场特征集的信息作为输入,提取出航线区域的特征值,并使用提取后的特征值选出重力匹配导航的适配区。选取高精度卫星测高全球海洋重力场模型,分别针对大西洋和太平洋海域的577个5°×5°子区域,每一个子区域选取100条航线进行不同条件下的重力匹配导航试验。试验结果显示:重力场深层感知模型与平均定位误差的斯皮尔曼相关系数的范围是0.61~0.91,肯德尔相关系数的范围是0.41~0.74。传统的多属性决策法与平均定位误差的斯皮尔曼相关系数的范围是0.22~0.70,肯德尔相关系数的范围是0.20~0.63。试验结果表明,提出的重力场深层感知模型提取的特征值与不同条件下重力匹配定位误差具有高度的相关性,因此,按照重力场深层感知模型特征提取值等级选取的区域,对应了相应等级的重力匹配导航定位精度,通过这样的方式提高了重力匹配导航适配区选取的准确性。
Objectives:The positioning accuracy of gravity-aided navigation is closely related to the distri⁃bution of the gravitational field in the navigation area.Therefore,the selection of the navigation adaptation area is critical to ensuring the performance of gravity-aided navigation.To ensure high positioning accuracy in gravity-aided navigation,we propose a gravity-aided navigation adaptation area selection algorithm based on a deep gravity field perception model,aiming to improve the accuracy of selecting the navigation adaptation area.Methods:First,the gravity field feature set is constructed using the gray-level co-occurrence matrix,local binary pattern,and the normalized gravity scale proposed in this paper.Second,a deep gravity field perception model is designed,and an inter-layer propagation mechanism is proposed for training the model.Finally,the information from the gravity field feature set is used as input to extract the feature values of the navigation area,and the selected feature values are used to choose the gravity-aided naviga⁃tion adaptation area.We select a high-precision satellite altimetry-based global ocean gravity field model,and conduct gravity-aided navigation experiments under different conditions for 577 sub-regions(5°×5°each)in the Atlantic and Pacific Oceans.For each sub-region,100 navigation routes are selected.Results:The experimental results show that the Spearman correlation coefficient between the deep gravity field per⁃ception model and the average positioning error ranges from 0.61 to 0.91,while the Kendall correlation coefficient ranges from 0.41 to 0.74.In contrast,the Spearman correlation coefficient between the traditional multi-attribute decision-making method and the average positioning error ranges from 0.22 to 0.70,and the Kendall correlation coefficient ranges from 0.20 to 0.63.Conclusions:The statistical relationship be⁃tween the feature extraction values of the deep gravity field perception model and the average positioning er⁃ror,based on a large number of
作者
毛国丞
武凛
席梦寒
刘石
熊常亮
鲍李峰
王勇
MAO Guocheng;WU Lin;XI Menghan;LIU Shi;XIONG Changliang;BAO Lifeng;WANG Yong(School of Geography and Information Engineering,China University of Geosciences(Wuhan),Wuhan 430074,China;Innovation Academy for Precision Measurement Science and Technology,Chinese Academy of Sciences,Wuhan 430077,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《武汉大学学报（信息科学版）》
EI
CAS
CSCD
北大核心
2024年第11期2122-2135,2144,I0006,共16页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金(42274116,42174102,41931076,42192535)
海南省重点研发计划(ZDYF2024SHFZ086)。
关键词
重力匹配导航适配区选取
多层感知机
重力场深层感知模型
灰度共生矩阵
局部二值模式
selection of adaptation areas for gravity matching navigation
multi-layer perception
gravita⁃tional deep perception model
gray level co-occurrence matrix
local binary pattern
