Multiplicative modified Rodrigues-parameters-based strong tracking unscented Kalman filter for satellite attitude estimation

Abstract

In this paper, an improved strong tracking unscented Kalman filter (STUKF) based on multiplicative modified Rodrigues parameters (MRPs) is proposed for satellite attitude estimation. The multiplicative MRPs are superior to additive ones in terms of attitude representation, especially when attitude angles are large. By minimizing the loss function in Wahba’s problem, a novel method of weighted average for MRPs is derived to replace the simple procedure. The generation of Sigma points, update of state variables and calculation of covariance matrices are all different from the existing literature to maintain the multiplicative property of MRPs. Simulation results by raw telemetry data from the on-orbit CubeSat Enlai-1 demonstrate the excellent performance of the proposed filter under large attitude angles.

A meticulous covariance adaptive Kalman filter for satellite attitude estimation

“…Recently, the robust Kalman filters based on the state-space model constructed by student-t have been widely studied for the heavytailed noises problem [22,23]. Additionally, several widely used and improved adaptive filters include the Sage-Husa adaptive Kalman filters [24,25], and adaptive filters based on innovation or residual covariance matching [26][27][28][29]19] proposed an adaptive robust Kalman filter with both adaptive and robust performance, the algorithm recursively makes a choice among the standard, robust, and adaptive strategies, taking into account abnormal innovation sequences and incorporating observations from the next moment, realizing the improvement of the attitude estimation accuracy in multiple cases; the best invariant quadratic unbiased estimation is introduced to adaptively tune and estimate the local components of the system's covariance at each step, thereby further enhancing the accuracy of attitude determination [6]; for the small-area or full-area infrared interference faced by the system, literature [30] proposes an adaptive fault-tolerant extended Kalman filter (AFTEKF), which adjusts the values of the relevant parameters by counting the number of faulty points diagnosed by the fault diagnostic algorithm, which ultimately improving the adaptive capability to infrared disturbances; recently [31], introduced a strong tracking filter in the multiplicative MRPs-based unscented Kalman filter to enhance the robustness of the satellite attitude system to model uncertainties; literature [32] used short-term sequences of the residual to represent the measurement disturbances, and developed an hidden Markov model recognizer for identifying the measurement disturbances and adaptively adjust the noise covariance of the MEKF, this process aims to improve the stability and accuracy of the attitude estimation system. However, the adaptive range of the above algorithms is narrow, and all of them ignore the interference caused by non-Gaussian noise and anomalies in vector measurement on the attitude estimation system.…”