Saturn's seasonal atmosphere

Fletcher, Leigh N.

doi:10.1093/astrogeo/atx138

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the final mission of the Cassini orbiter, this space shuttle faced its burn‐up destruction in the atmosphere of Saturn, on 15 September 2017 [1, 2]. In the short modern technological history, realising the long‐term voyage of a spacecraft has been extensively investigated.…”

Section: Introductionmentioning

confidence: 99%

Recursive linear continuous quaternion attitude estimator from vector observations

Zhou

Fourati

et al. 2018

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

Attitude estimation from vector observations is widely employed in aerospace applications for accurate integrated navigation using solutions to Wahba's problem. Wahba's solutions are practical but may corrupt facing critical cases in the presence of almost collinear reference vector measurements, which is inevitable in robotic applications with redundant sensor arrays or platforms with celestial vision sensors in similar directions. Different from existing algorithms, this paper presents a novel sequential multiplicative quaternion attitude estimation method from various vector sensor outputs. The unique linear constitution of the algorithm leads to its specific name of Recursive Linear Quaternion Estimator (RLQE). The algorithm's architecture is designed to use each single pair of vector observation linearly so that the vector observations can be arbitrarily chosen and fused. The closed-form covariance of the RLQE is derived that builds up the existence of a highly reliable RLQE Kalman filter (RLQE-KF). Simulations and experiments are carried out to give the performances of our algorithm and representative ones. Compared with other works, the proposed RLQE maintains good precision, better consistency and lower variance bounds. Moreover, the attitude estimation performance with critical cases is especially much better than conventional Wahba's solution on its continuity, accuracy and variance.

show abstract

Section: Introductionmentioning

confidence: 99%