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作 者:秦洪懋 王希 王广才 胡满江 秦晓辉 Qin Hongmao;Wang Xi;Wang Guangcai;Hu Manjiang;Qin Xiaohui(State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle,College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China;Wuxi Intelligent Control Research Institute of Hunan University,Wuxi 214115,China)
机构地区:[1]湖南大学机械与运载工程学院,整车先进设计制造全国重点实验室,长沙410082 [2]湖南大学无锡智能控制研究院,无锡214115
出 处:《战术导弹技术》2023年第4期37-47,68,共12页Tactical Missile Technology
基 金:国家重点研发计划(2021YFC2801700);国防基础科研计划(JCKY2021110B024,JCKY2022110C072);湖南省科技创新计划(2021RC2064);中国博士后科学基金(2022M721096)。
摘 要:针对水下环境复杂,传感器异常值较多,传统卡尔曼滤波定位方法难以实现精确定位的问题,提出一种基于因子图优化(FGO)的紧耦合INS/USBL/DVL方案和核心融合算法。采用FGO方法构建紧耦合INS/USBL/DVL因子图模型,加入粗差检测函数调整各因子的权重,从而提高水下传感器信息利用率,改善了水下导航精度及可靠性,并通过仿真及湖泊试验证实了该方案的合理性。结果表明,所提出方法相比于KF-LC、KF-TC和FGO-LC方法,水平位置精度分别提高了29.2%、6.6%和13.2%,且在出现异常传感器信息时有较为平顺的导航误差表现。In view of challenges of complex underwater environments with a high occurrence of sensor outliers,traditional Kalman-filter-based localization methods are difficult to achieve precise positioning.To address this issue,a factor graph optimization(FGO)-based tightly-coupled INS/USBL/DVL scheme and core fusion algorithm are proposed.The FGO method is used for constructing a tightly-coupled INS/USBL/DVL factor graph model,and a weight function is added to adjust the weights of each factor,thereby improving the utilization of underwater sensor information and enhancing the accuracy and reliability of underwater navigation.The feasibility of the proposed scheme is verified through simulations and lake tests.The results show that the proposed method outperforms KF-LC,KF-TC,and FGO-LC methods,with the horizontal position accuracy respectively increased by 29.2%,6.6%,and 13.2%.Moreover,the proposed method exhibits smooth navigation error performance in the presence of abnormal sensor information.
关 键 词:超短基线导航系统 多普勒测速仪 紧耦合策略 图优化 鲁棒性 水下导航 信息融合
分 类 号:U666.1[交通运输工程—船舶及航道工程]
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