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基于GPS观测量和模型预测滤波的机载SINS/GPS空中自对准
  • ISSN号:1005-6734
  • 期刊名称:《中国惯性技术学报》
  • 时间:0
  • 分类:U666.1[交通运输工程—船舶及航道工程;交通运输工程—船舶与海洋工程]
  • 作者机构:[1]北京航空航天大学仪器科学与光电工程学院,北京100083
  • 相关基金:国家863计划项目(2006AA12A108):国家自然科学基金(60602047).
中文摘要:

针对机载SINS/GPS组合导航系统地面静基座对准时间较长的问题,提出了一种基于GPS观测量和模型预测滤波(MPF)的机载SINS/GPS空中开机自对准方法。该方法首先在载机匀速直线飞行阶段进行SINS/GPS空中开机粗对准,利用GPS获得初始位置、速度和航向,利用加速度计的输出信息计算两个初始水平姿态角;然后在载机进入最优S机动飞行段进行SINS空中精对准,采用MPF和EKF相结合的滤波器估计SINS的误差并进行校正。计算机仿真结果表明,该方法实现了SINS的空中开机自对准,大大缩短飞机的地面准备时间,空中开机粗对准的方位角误差小于15°,俯仰角和横滚角误差小于2°,而空中精对准的方位角、俯仰角和横滚角的估计误差分别达到了67.36〞、47.31〞和-32.52〞。

英文摘要:

In view of the long alignment time of ground static base for airborne SINS/GPS, an in-flight start-up auto-alignment method for airborne SINS/GPS was presented which was based on GPS observations and model predict filter(MPF). The method started with the in-flight start-up coarse alignment during the uniform straight-line motion of aircraft, in which GPS was used to obtairi the initial position, velocity and azimuth of SINS, and the accelerometers's output information was used to calculate the initial pitch and roll. Then the fine in-flight alignment is carried out during the S flight of aircraft in which an MPF combined with the extended Kalman filter was used to estimate the error of SINS due to the in-flight coarse alignment. Simulation results indicate that the in-flight starting and alignment method are feasible. The heading misalignment, pitch misalignment and roll misalignment of in-flight coarse alignment are less than 15° , 2° and 2° respectively, and the heading misalignment, pitch misalignment and roll misalignment of in-flight fine alignment are 67.36′, 47.31′ and -32.52′ respectively, which greatly reduces the get-away time of aircraft.

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期刊信息
  • 《中国惯性技术学报》
  • 中国科技核心期刊
  • 主管单位:中国科学技术协会
  • 主办单位:中国惯性技术学会
  • 主编:刘飞
  • 地址:天津市63信箱75分箱
  • 邮编:300131
  • 邮箱:gxjsxb@126.com
  • 电话:022-26032791
  • 国际标准刊号:ISSN:1005-6734
  • 国内统一刊号:ISSN:12-1222/O3
  • 邮发代号:
  • 获奖情况:
  • 多次获中国科协期刊优秀论文奖
  • 国内外数据库收录:
  • 波兰哥白尼索引,荷兰文摘与引文数据库,美国工程索引,美国剑桥科学文摘,英国科学文摘数据库,中国中国科技核心期刊,中国北大核心期刊(2008版),中国北大核心期刊(2011版),中国北大核心期刊(2014版)
  • 被引量:7493