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机构地区:[1]武汉大学遥感信息工程学院,湖北武汉430079 [2]Department of Civil and Environmental Engineering and Geodetic Science,The Ohio State University
出 处:《测绘学报》2010年第1期28-33,共6页Acta Geodaetica et Cartographica Sinica
基 金:国家自然科学基金(40501060;40771176)
摘 要:基于随机常数、随机游走和一阶马尔可夫过程组合的高阶IMU误差模型,建立36阶卡尔曼滤波器。通过松散组合模式,最终实现位置标准差±5cm、俯仰/横滚角标准差±0.002°、航向角标准差±0.008°的定位定向精度。Despite significant progress in GSP/INS-based direct georeferencing technology in the last decade, there is still room for improvement in terms of better accuracy. The main objective of this paper is the stochostic modeling of Inertial Measurement Unit (IMU) errors, which determines significantly the final performance of the GPS/INS integrated system. With the random constants, random walks and first-order Markov process models to characterize the stochastic errors oflMU, one Kalman Filter with 36 orders was meticulously established. The practical data was processed with the filter in the experiment and the accuracy in term of standard deviations of 5 cm in position, 0.002% in attitude and 0.008% in heading were achieved respectively.
关 键 词:直接地理参考 直接平台定向 航空三角测量 GPS/INS集成 卡尔曼滤波
分 类 号:P228[天文地球—大地测量学与测量工程]
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