Recursive linear continuous quaternion attitude estimator from vector observations. Issue 11 (4th September 2018)

• Record Type: Journal Article
• Authors: Wu, Jin; Zhou, Zebo; Fourati, Hassen; Liu, Ming
• Is Part Of: IET radar, sonar & navigation. Volume 12:Issue 11(2018)
• Page Start: 1196
• Page End: 1207
• Publication Date: 2018-09-04
• Subjects: Kalman filters -- image sensors -- inertial navigation -- attitude measurement -- recursive estimation -- aerospace control -- covariance analysis; vector observation -- attitude estimation performance -- Recursive linear continuous quaternion attitude estimator -- Wahba's solutions -- collinear reference vector measurements -- redundant sensor arrays -- celestial vision sensors -- vector sensor -- unique linear constitution -- aerospace applications -- Wahba's problem -- robotic applications -- sequential multiplicative quaternion attitude estimation -- closed‐form covariance -- RLQE Kalman filter; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2018.5028
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : 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 study 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. Simulations and experiments are carried out to give the performances of the authors' 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.