基于逆向导航解算和数据融合的SINS传递对准方法

发布时间：2018-02-22 11:48

本文关键词： 捷联惯性导航系统传递对准数据融合逆向—正向导航解算　出处：《中国惯性技术学报》2015年06期 　论文类型：期刊论文

【摘要】：提出两种观点:1)可以使用一段惯性传感器数据完成捷联惯性导航系统(SINS)的初始对准;2)通过在数据融合中加入逆向—正向SINS解算结果和外部参考数据,可以缩短传感器误差的估计时间。基于以上两观点,旨在不改变卡尔曼滤波器的情况下,短时间内估计陀螺漂移的一种快速传递对准的方法。在一个参考数据更新周期内,储存了惯性传感器数据和参考数据,并且执行了逆向—正向捷联解算。与此同时,当相应的捷联解算结束后,执行数据融合算法。在上述的更新周期内,由于加入逆向—正向捷联解算,陀螺漂移估计操作增加了两次并且缩短了其估计时间。在舰船晃动的情况下,实验表明:与经典的方法相比,当两种方法的数据融合执行次数相等时,提出的方法缩短了对准时间,对准时间从300 s缩短到100 s;当对准时间相等时,方案1的纵摇、横摇和航向对准误差的均值分别为?0.6638、?0.5896、0.3941,其方差分别为1.0736、0.4629、2.1697,而方案2对应的均值和方差分别为?0.4632、?0.5026、0.3375和0.8828、0.3876、1.7289。由此可知,方案2的对准精度高于方案1。
[Abstract]:This paper presents two views: 1) the initial alignment of sins can be completed by using a segment of inertial sensor data)) by adding the inverse forward SINS solution and external reference data to the data fusion. Based on the above two viewpoints, a fast transfer alignment method for estimating gyro drift in a short time without changing the Kalman filter is proposed. The inertial sensor data and reference data are stored, and the reverse forward strapdown solution is performed. At the same time, when the corresponding strapdown solution is completed, the data fusion algorithm is executed. With the addition of inverse forward strapdown solution, the gyro drift estimation operation is increased twice and the estimation time is shortened. In the case of ship sloshing, the experimental results show that, compared with the classical method, When the data fusion times of the two methods are equal, the proposed method shortens the alignment time from 300 s to 100 s, and when the alignment time is equal, the mean values of pitch, roll and heading alignment errors of scheme 1 are? 0.6638,? The variance of 0.5896 / 0.3941 was 1.0736U 0.46292.1697, respectively, while the mean and variance of scheme 2 were respectively? 0.4632,? 0.5026, 0.3375 and 0.8828, 0.38766 and 1.7289. It can be seen that the alignment accuracy of scheme 2 is higher than that of scheme 1.
【作者单位】：东南大学仪器科学与工程学院微惯性仪表与先进导航技术教育部重点实验室;
【基金】：国家自然科学基金项目(61473085,51175082,51375088,61273056)
【分类号】：TN96

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