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首页> 外文期刊>Advances in space research >Nonlinear filtering for sequential spacecraft attitude estimation with real data: Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter
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Nonlinear filtering for sequential spacecraft attitude estimation with real data: Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter

机译：用于使用实际数据进行连续航天器姿态估计的非线性滤波：Cubature Kalman滤波，Unscented Kalman滤波和Extended Kalman滤波

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This article compares the attitude estimated by nonlinear estimator Cubature Kalman Filter with results obtained by the Extended Kalman Filter and Unscented Kalman Filter. Currently these estimators are the subject of great interest in attitude estimation problems, however, mostly the Extended Kalman Filter has been applied to real problems of this nature. In order to evaluate the behavior of the Extended Kalman Filter, Unscented Kalman Filter and Cubature Kalman Filter algorithms when submitted to realistic situations, this paper uses real data of sensors on-board the CBERS-2 remote sensing satellite (China Brazil Earth Resources Satellite). It is observed that, for the case studied in this article, the filters are very competitive and present advantages and disadvantages that should be dealt with according to the requirements of the problem. (C) 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.
机译：本文将非线性估计器Cubature卡尔曼滤波器估计的姿态与扩展卡尔曼滤波器和无味卡尔曼滤波器获得的结果进行了比较。当前，这些估计器是姿态估计问题中非常感兴趣的主题，但是，大多数扩展卡尔曼滤波器已应用于这种性质的实际问题。为了评估在现实情况下扩展卡尔曼滤波，无味卡尔曼滤波和库珀卡尔曼滤波算法的行为，本文使用CBERS-2遥感卫星（中国巴西地球资源卫星）上传感器的真实数据。可以看出，对于本文中研究的情况，过滤器具有很强的竞争力，并存在应根据问题的要求加以处理的优点和缺点。 （C）2018年COSPAR。由Elsevier Ltd.出版。保留所有权利。

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