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为了在重力异常特征微弱区域内实现重力辅助导航以及提高惯性导航系统在重力异常特征明显区域内的定位精度和匹配率,提出了基于支持向量机的重力匹配算法。研究了支持向量机学习样本的选取、支持向量机参数和重力粗糙度的关系,构造了用于重力匹配算法的支持向量机。经计算仿真研究表明,通过选择适当的支持向量机参数,可以实现重力辅助导航,算法在重力特征显著的区域具有较高的匹配率,组合导航系统的定位误差在一个重力图网格左右。
In order to realize gravity-assisted navigation in the weak area of gravity anomalies and to improve the positioning accuracy and matching rate of the inertial navigation system in a clear area with gravity anomalies, a gravity matching algorithm based on support vector machines is proposed. The selection of SVM learning samples, the relationship between support vector machine parameters and gravitational roughness are studied, and a support vector machine for gravity matching algorithm is constructed. The results of computational simulation show that gravity-assisted navigation can be achieved by selecting proper SVM parameters. The algorithm has a high matching rate in regions with significant gravity features. The positioning error of integrated navigation system is about a grid of gravity maps.