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作 者:林雪原 王萍[1] 许家龙 刘立宁[1] 陈祥光[1,3] LIN Xueyuan;WANG Ping;XU Jialong;LIU Lining;CHEN Xiangguang(College of Engineering,Yantai Nanshan University,12 Daxue Road,Longkou 265713,China;Aerosun Corporation,188 Mid-Tianyuan Road,Nanjing 211100,China;School of Chemistry and Chemical Engineering,Beijing Institute of Technology,5 South-Zhongguancun Street,Beijing 100081,China)
机构地区:[1]烟台南山学院工学院,山东省龙口市265713 [2]航天晨光股份有限公司,南京市211100 [3]北京理工大学化学与化工学院,北京市100081
出 处:《大地测量与地球动力学》2022年第12期1211-1215,共5页Journal of Geodesy and Geodynamics
基 金:国家自然科学基金(60874112,61673208);山东省自然科学基金(2016ZRA06068)。
摘 要:组合导航系统在动态环境下具有强非线性,为提高GNSS/CNS/SINS组合导航系统的导航精度,提出一种基于序贯UKF的多传感器最优融合算法。首先,建立GNSS/CNS/SINS组合导航系统的非线性状态方程及2个子滤波器的线性量测方程;然后,对标准UKF的量测更新过程进行简化,简化UKF算法具有与标准UKF算法相同的滤波精度,且具有计算量低的特性;最后,将序贯滤波算法与简化UKF算法结合,提出多传感器组合导航系统的序贯UKF最优融合算法。仿真结果表明,本文序贯UKF算法不仅可提高系统解算的实时性,并且相对于集中线性卡尔曼滤波算法及经典集中线性UKF算法具有较高的滤波精度。Integrated navigation system has strong nonlinear characteristic in dynamic environment,so we propose an optimal multi-sensor fusion algorithm based on sequential UKF to improve the navigation accuracy of GNSS/CNS/SINS integrated navigation system.Firstly,we establish the nonlinear state equation and the linear measurement equations of two subfilters for GNSS/CNS/SINS integrated navigation system.Then,by simplifying the measurement updating process of the standard UKF algorithm,we design a simplified UKF algorithm that has the same filtering accuracy as the standard UKF algorithm and has characteristics of low computation.Finally,we propose the sequential UKF optimal fusion algorithm for multi-sensor integrated navigation system,by combining the sequential filtering algorithm with the simplified UKF algorithm.The simulation results show that the sequential UKF algorithm not only improves the real-time performance of the system,but also has higher filtering accuracy than the conventional centralized Kalman filter algorithm and the classical centralized linear UKF algorithm.
关 键 词:简化UKF 序贯UKF 多传感器组合导航 集中常规卡尔曼滤波算法 经典集中线性UKF算法
分 类 号:P228[天文地球—大地测量学与测量工程]
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