The adaptive Gaussian mixtures unscented Kalman filter for attitude determination using light curves

The Adaptive Gaussian Mixtures Unscented Kalman Filter (AGMUKF) is introduced to estimate the attitude of a Resident Space Object using light curves. This filter models the state probability density function as a Gaussian Mixture. This enables to capture the non-linearities of the light-curve measurement model. A non-linearity index is used to refine the mixture when necessary, and individual Gaus-sian kernels are merged back together when their relative distance is below a certain threshold. A conventional attitude Unscented Kal-man Filter (UKF) is used to propagate and update each kernel. The AGMUKF efficiently maintains the mixture population as low as possible, while still being able to represent non-symmetric, multimodal, arbitrarily complex distributions. Therefore, it is presented as a promising alternative to Particle-Filter-based implementations, the current state of the art used in sequential attitude estimation from light curves. The non-linearity index has been used to show that the measurement model is the main contributor to the system non -linearity. Results have demonstrated the superiority of the AGMUKF w.r.t. the UKF for attitude determination, and that it can con-verge for high initial state uncertainty cases, successfully capturing the non-Gaussian probability distribution of the state. (c) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.