This paper describes an algorithm obtained by merging a recursive star-identification algorithm with a recently developed adaptive singular-value-decomposition-based estimator of the angular-velocity vector (QuateRA). In a recursive algorithm, the more accurate the angular-velocity estimate is, the quicker and more robust to noise is the resultant recursive algorithm. 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 reorientation maneuver, and a satellite undergoing non-pure-spin dynamics.

本文介绍了一种算法，该算法是通过将递归恒星识别算法与最近开发的基于自适应奇异值分解的角速度矢量估计器（QuateRA）合并而成的。在递归算法中，角速度估计值越准确，所得的递归算法对噪声的响应就越快且越鲁棒。因此，将这两种技术结合起来可以产生一种能够处理各种动态情况的算法。在选择的模拟场景中突出显示了算法的速度和鲁棒性。首先，使用最先进的空间迷失恒星识别算法Pyramid进行速度比较。该测试表明，在最佳情况下，该算法平均比金字塔快一个数量级。下一个，