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Geomagnetic matching navigation algorithm based on robust estimation

机译:基于鲁棒估计的地磁匹配导航算法

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The outliers in the geomagnetic survey data seriously affect the precision of the geomagnetic matching navigation and badly disrupt its reliability. A novel algorithm which can eliminate the outliers influence is investigated in this paper. First, the weight function is designed and its principle of the robust estimation is introduced. By combining the relation equation between the matching trajectory and the reference trajectory with the Taylor series expansion for geomagnetic information, a mathematical expression of the longitude, latitude and heading errors is acquired. The robust target function is obtained by the weight function and the mathematical expression. Then the geomagnetic matching problem is converted to the solutions of nonlinear equations. Finally, Newton iteration is applied to implement the novel algorithm. Simulation results show that the matching error of the novel algorithm is decreased to 7.75% compared to the conventional mean square difference (MSD) algorithm, and is decreased to 18.39% to the conventional iterative contour matching algorithm when the outlier is 40nT. Meanwhile, the position error of the novel algorithm is 0.017° while the other two algorithms fail to match when the outlier is 400nT.
机译:地磁调查数据中的异常值严重影响了地磁匹配导航的精度,严重破坏了其可靠性。在本文中研究了一种可以消除异常值影响的新算法。首先,设计重量函数,并引入了其稳健估计的原理。通过将匹配轨迹与参考轨迹之间的关系方程与用于地磁信息的泰勒级扩展组合,获取了经度,纬度和前置误差的数学表达。通过权重函数和数学表达式获得鲁棒目标功能。然后将地理匹配问题转换为非线性方程的解。最后,牛顿迭代应用于实施新颖算法。仿真结果表明,与常规均方差(MSD)算法相比,新型算法的匹配误差减少到7.75%,并且当异常值为40nt时,传统迭代轮廓匹配算法减少至18.39%。同时,新颖算法的位置误差为0.017°,而当异常值为400nt时,其他两个算法无法匹配。

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