Simplex Back Propagation Estimation Method for Out-of-Sequence Attitude Sensor Measurements

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 20; p. 7970
• Main Authors: Goh, Shu Ting, Tissera, M. S. C., Tan, RongDe Darius, Srivastava, Ankit, Low, Kay-Soon, Lim, Lip San
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022; MDPI
• Subjects: Accuracy; Actuators; Algorithms; attitude estimation; Back propagation; Bias; Communication; Complexity; Covariance; Data acquisition; Digital signal processors; Error analysis; Extended Kalman filter; Global positioning systems; GPS; Interfaces; Kalman filter; Measurement; Methods; Microelectromechanical systems; Microprocessors; out-of-sequence measurement; Propagation; Satellite attitude control; Satellite buses; Satellites; Sensors; small satellite; Small satellites; Transmitters
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22207970

For a small satellite, the processor onboard the attitude determination and control system (ADCS) is required to monitor, communicate, and control all the sensors and actuators. In addition, the processor is required to consistently communicate with the satellite bus. Consequently, the processor is unable to ensure all the sensors and actuators will immediately respond to the data acquisition request, which leads to asynchronous data problems. The extended Kalman filter (EKF) is commonly used in the attitude determination process, but it assumes fully synchronous data. The asynchronous data problem would greatly degrade the attitude determination accuracy by EKF. To minimize the attitude estimation accuracy loss due to asynchronous data while ensuring a reasonable computational complexity for small satellite applications, this paper proposes the simplex-back-propagation Kalman filter (SBPKF). The proposed SBPKF incorporates the time delay, gyro instability, and navigation error into both the measurement and covariance estimation during the Kalman update process. The performance of SBPKF has been compared with EKF, modified adaptive EKF (MAEKF), and moving–covariance Kalman filter (MC-KF). Simulation results show that the attitude estimation error of SBPKF is at least 30% better than EKF and MC-KF. In addition, the SBPKF’s computational complexity is 17% lower than MAEKF and 29% lower than MC-KF.