### Abstract

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.

Original language | English |
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Title of host publication | AIAA Guidance, Navigation, and Control Conference 2015, MGNC 2015 - Held at the AIAA SciTech Forum 2015 |

Publisher | American Institute of Aeronautics and Astronautics Inc. |

ISBN (Electronic) | 9781510801097 |

Publication status | Published - 2015 |

Event | AIAA Guidance, Navigation, and Control Conference 2015, MGNC 2015 - Held at the AIAA SciTech Forum 2015 - Kissimmee, United States Duration: 2015 Jan 5 → 2015 Jan 9 |

### Other

Other | AIAA Guidance, Navigation, and Control Conference 2015, MGNC 2015 - Held at the AIAA SciTech Forum 2015 |
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Country | United States |

City | Kissimmee |

Period | 15/1/5 → 15/1/9 |

### Fingerprint

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Aerospace Engineering
- Control and Systems Engineering

### Cite this

*AIAA Guidance, Navigation, and Control Conference 2015, MGNC 2015 - Held at the AIAA SciTech Forum 2015*American Institute of Aeronautics and Astronautics Inc..

**Receding-horizon unscented Kalman filter using successive unscented transformation for spacecraft attitude estimation.** / Hirasawa, Ryo; Nakajima, Yuta; Takahashi, Masaki.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*AIAA Guidance, Navigation, and Control Conference 2015, MGNC 2015 - Held at the AIAA SciTech Forum 2015.*American Institute of Aeronautics and Astronautics Inc., AIAA Guidance, Navigation, and Control Conference 2015, MGNC 2015 - Held at the AIAA SciTech Forum 2015, Kissimmee, United States, 15/1/5.

}

TY - GEN

T1 - Receding-horizon unscented Kalman filter using successive unscented transformation for spacecraft attitude estimation

AU - Hirasawa, Ryo

AU - Nakajima, Yuta

AU - Takahashi, Masaki

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84973470868&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84973470868&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84973470868

BT - AIAA Guidance, Navigation, and Control Conference 2015, MGNC 2015 - Held at the AIAA SciTech Forum 2015

PB - American Institute of Aeronautics and Astronautics Inc.

ER -