| 扩展卡尔曼滤波的虚拟格网伪距差分算法研究 |
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| 引用本文： | 肖青怀,陈秉柱,谷守周,秘金钟,常军.扩展卡尔曼滤波的虚拟格网伪距差分算法研究[J].测绘科学,2021,46(2):71-77. |
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| 作者姓名： | 肖青怀 陈秉柱 谷守周 秘金钟 常军 |
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| 作者单位： | 中国测绘科学研究院,北京100036;中国测绘科学研究院,北京100036;山东科技大学,山东青岛266590;自然资源部第一大地测量队,西安710054 |
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| 基金项目： | 国家重点研发计划课题项目(2016YFB0501801,2016YFB0502105,2016YFB0502101);全球连续监测评估系统项目(CFZX0301040308-06);中国测绘科学研究院基本科研业务费项目(AR1901);湖南省自然资源调查与监测工程技术研究中心开放课题(2020-10)。 |
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| 摘 要： | 针对传统伪距差分服务端压力大,以及在复杂环境下进行导航定位,某些历元卫星信号弱、卫星数不足、无法连续定位的问题,该文提出了基于扩展卡尔曼滤波算法的虚拟格网伪距差分方法。该方法充分利用先验信息和动力学模型,解决了复杂环境中动态定位结果不连续、定位精度低等问题。为验证算法的有效性,该文分别进行了动态、静态实验,并与最小二乘结果进行对比,实验结果表明:静态模式下,卡尔曼滤波算法比最小二乘算法的定位精度,在N、E、U方向分别提高48.3%、47.1%、52.5%;动态模式下,卡尔曼滤波算法比最小二乘算法更加稳定,更适合复杂环境定位。
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| 关 键 词： | 卡尔曼滤波 虚拟格网 伪距差分 最小二乘 |
Research on virtual grid pseudorange difference algorithm based on extended Kalman filter |
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| XIAO Qinghuai,CHEN Bingzhu,GU Shouzhou,BEI Jinzhong,CHANG Jun.Research on virtual grid pseudorange difference algorithm based on extended Kalman filter[J].Science of Surveying and Mapping,2021,46(2):71-77. |
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| Authors: | XIAO Qinghuai CHEN Bingzhu GU Shouzhou BEI Jinzhong CHANG Jun |
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| Institution: | (Chinese Academy of Surveying and Mapping,Beijing 100036,China;Shandong University of Science and Technology,Qingdao,Shandong 266590,China;The First Geodetic Surveying Brigade of Ministry of Natural Resources,Xi’an 710054,China) |
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| Abstract: | In view of the high pressure of the traditional pseudo-range differential server,and the navigation and positioning in a complex environment,some epoch satellite signals are weak,the number of satellites is insufficient,and continuous positioning is impossible,a virtual grid pseudo-grid based on the extended Kalman filter algorithm was proposed.This method made full use of prior information and dynamic model to solve the problems of discontinuous dynamic positioning results and low positioning accuracy in complex environments.In order to verify the effectiveness of the algorithm,this paper conducted dynamic and static experiments respectively,and compared with the least squares results.The experimental results showed that in static mode,the Kalman filter algorithm had higher positioning accuracy than the least squares algorithm by 48.3%,47.1%,and 52.5% in the N,E,and U directions,respectively;in dynamic mode,the Kalman filter algorithm was more stable than the least squares algorithm,and was more suitable for positioning in complex environments. |
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| Keywords: | Kalman filter virtual grid pseudorange difference least squares |
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