Receding-horizon unscented kalman filter using successive unscented transformation for spacecraft attitude estimation

Ryo Hirasawa, Yuta Nakajima, Masaki Takahashi

研究成果: Conference contribution

1 被引用数 (Scopus)


A new attitude estimation method for a spacecraft is derived. This method employs a receding-horizon strategy to use a time window and constraint. A conventional constrained filter, the receding-horizon nonlinear Kalman filter (RNKF), propagates the state value in the prediction step, and minimizes the cost function with a constraint in the filtering step. It is desirable for the optimization to be a quadratic programming (QP) problem, whose constraint is linear, in terms of computational complexity. If the RNKF is applied to the attitude estimation problem, the appropriate attitude representation is the quaternion, which has no singular point, in the prediction step. However, the quaternion does not define a QP problem in the filtering step because the quaternion needs to satisfy a single constraint of a unit norm. Therefore, this paper proposes the receding-horizon unscented Kalman filter (RUKF), which is an improvement of the RNKF, to deal with appropriate attitude representation in each step. In the RUKF, each attitude of a time window is represented by generalized Rodrigues parameters (GRPs) in the filtering step employing the successive unscented transformation. The GRPs is an attitude representation with no constraint. Simulation revealed that the RUKF is more accurate than the extended Kalman filter.

ホスト出版物のタイトルAIAA Guidance, Navigation, and Control Conference
出版社American Institute of Aeronautics and Astronautics Inc, AIAA
出版ステータスPublished - 2015
イベントAIAA Guidance, Navigation, and Control Conference, 2015 - Kissimmee, United States
継続期間: 2015 1月 52015 1月 9


OtherAIAA Guidance, Navigation, and Control Conference, 2015
国/地域United States

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 航空宇宙工学
  • 制御およびシステム工学


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