一种提高地形辅助导航精度的通用导航地图绘制方法

S. Reynaud, C. Louis
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引用次数: 3

摘要

提高地形辅助导航(TAN)精度的一个有趣的方法可能是找到最适合收集地形传感器信息的最佳轨迹。本文提出了一种基于Cramer - Rao下界(CRLB)的新准则来提高TAN精度的通用方法。在一些假设条件下,地形对导航精度的局部贡献可以从信息解释的CRLB递归表达式中提取出来。在计算规则间隔网格的每个节点时,也可以定义一个导航图。这张地图可以用任何一种地球物理传感器计算每一种车辆。因此,即使在任务约束下,任何经典的路径规划算法都可以用来找到最大化累积导航得分的轨迹。本文通过三个不同的应用程序演示了该准则的主要功能。从一架装备了多波束扫描激光和错误嵌入式数字高程图的飞机上进行的精确空投应用显示了利用与地图相关的错误模型的能力。在gps拒绝环境中的船舶导航表明,该准则适用于任何类型的TAN,特别是具有重力异常图(重力测量)的导航。最后,一个高程地形跟踪应用表明,这种新准则的性能优于众所周知的和广泛使用的粗糙度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A universal navigability map building approach for improving Terrain-Aided-Navigation accuracy
An interesting way to improve Terrain-Aided- Navigation (TAN) accuracy may consist in finding the best trajectory which gathers the maximum of information coming from the terrain sensor. This paper proposes a universal approach for improving the TAN accuracy based on a new criterion derived from the fundamental Cramer Rao Lower Bound (CRLB). Under few hypotheses the local contribution of the terrain to the navigation accuracy can be extracted from a recursive expression of the CRLB interpreted in term of information. A navigability map can also be defined in computing this criterion at each node of a regularly spaced grid. This map can be computed for every kind of vehicle using any kind of geophysical sensor. Any classical path planning algorithm can thus be used to find trajectories maximizing the cumulated navigability score, even under mission constraints. This paper demonstrates the main capabilities of this criterion through three different applications. A precision airdrop application from an aircraft equipped with a multi-beam scanner laser and an erroneous embedded Digital Elevation Map shows the ability to take advantage of an error model associated to the map. A marine vessel navigation in a GPS-denied environment illustrates that this criterion performs well for any kind of TAN, in particular for a navigation with a gravity anomaly map (gravimetry). Finally an altimetry terrain following application exhibits this new criterion's ability to outperform the well-known and widely used roughness.
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