论文标题

使用基于自适应SVD的角速度估计器的递归星星识别算法

Recursive Star-Identification Algorithm using an Adaptive SVD-based Angular Velocity Estimator

论文作者

Johnston, Hunter, Leake, Carl, de Almeida, Marcelino M., Mortari, Daniele

论文摘要

本文介绍了一种通过将递归星星识别算法与最近开发的基于自适应SVD的角速度矢量(Quatera)合并而获得的算法。在递归算法中,角速度估计值越准确,噪声越快,越强大,则所得的递归算法IS。因此,结合这两种技术会产生一种能够处理各种动态场景的算法。在选择模拟方案中突出显示了算法的速度和鲁棒性。首先,与最先进的空间丢失星识别算法(金字塔)进行了比较。该测试表明,在最好的情况下,该算法平均比金字塔快的数量级。接下来,针对各种动态案例进行了递归算法,包括地面“恒星指南针”场景,地球同步轨道中的卫星,重新定位机动器期间的卫星以及卫星的卫星。

This paper describes an algorithm obtained by merging a recursive star identification algorithm with a recently developed adaptive SVD-based estimator of the angular velocity vector (QuateRA). In a recursive algorithm, the more accurate the angular velocity estimate, the quicker and more robust to noise the resultant recursive algorithm is. Hence, combining these two techniques produces an algorithm capable of handling a variety of dynamics scenarios. The speed and robustness of the algorithm are highlighted in a selection of simulated scenarios. First, a speed comparison is made with the state-of-the-art lost-in-space star identification algorithm, Pyramid. This test shows that in the best case the algorithm is on average an order of magnitude faster than Pyramid. Next, the recursive algorithm is validated for a variety of dynamic cases including a ground-based "Stellar Compass" scenario, a satellite in geosynchronous orbit, a satellite during a re-orientation maneuver, and a satellite undergoing non-pure-spin dynamics.

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