Strong Tracking Sigma Point Predictive Variable Structure Filter for Attitude Synchronisation Estimation

Abstract

In this paper, a novel Strong Tracking Sigma-Point Predictive Variable Structure Filter (ST-SP-PVSF) is presented as a further development of the Adaptive Predictive Variable Structure Filter (APVSF) for attitude synchronisation during Satellite Formation Flying (SFF). First, the sequence orthogonal principle is adopted to enhance the robustness of the APVSF for any nonlinear system with uncertain model errors. Then, sigma-point sampling strategies (such as unscented transfer, cubature rule and Stirling's polynomial interpolation) are introduced to extend the APVSF with the ability to capture the second central moment's information on the model errors to update the system model with higher precision. The new methodology has advantages in dealing with the various types of uncertainties or model errors compared with the APVSF. In addition, it does not need to choose the limit boundary layer ψlim it for system estimation, which reduces the sensitivity to the initial parameters and improves its adaptive ability over the APVSF. Simulations are performed to demonstrate that the proposed method is more suitable for attitude synchronisation estimation of the SFF system.

Developments of attitude determination and control system of microsats: A survey

“…More specifically, the system noise should comply with the white Gaussian distribution when using Kalman-type filters, while the noise level and its distribution cannot be obtained for satellites ahead of time. 25 In such case, other filters such as predictive filter (PF), [26][27][28] unscented predictive filter (UPF), 29,30 cubature predictive filter (CPF), 31,32 center difference predictive filter (CDPF), 33,34 and predictive variable structure filter (PVSF) [35][36][37][38] were proposed for attitude estimation. In the meantime, filters such as particle filter 39 and robust filter 40 can also be found in the literature.…”

A Framework of Cubature-Hᵢ/H∞-Fault Detection and Robust H-Infinity Kalman Filter of Ship SINS/GNSS Integrated System

“…However, all examples in the literature are based on innovation sequences, which may cause measurement noise or other covariances to be negative for the subtraction. Hence, strong tracking and its modification algorithms are usually supplied behind the adaption algorithms [32], [33]. Nevertheless, the aforementioned methods assume the measurements are reliable and the predicted residual vector or state vector discrepancy reflects the error magnitude of the dynamic model information; they cannot handle gross error caused by uncertain outliers.…”

Laplace ℓ1 robust Student’s T-filter for attitude estimation of satellites

“…Accordingly, many approximation methods are applied to design the suboptimal nonlinear filters such as the classical Kalman filter (KF) and its extensions. [9][10][11][12][13][14] For those filters, both the system states and the measurement predictions are assumed to have Gaussian characteristics. Considering space missions such as the orbit tracking or the attitude determination, the noise and the unknown perturbations may be induced.…”