Rao-Blackwellized particle filter with vector observations for satellite three-axis attitude estimation and control in a simulated testbed

Abstract

RESUMOFiltro de partículas Rao-Blackwell com medidas vetoriais para estimação e controle em 3 eixos da atitude de satélite testado em simulação de plataforma posicionadora. Um filtro de partículas Rao-Blackwell foi projetado e seu desempenho investigado mediante simulação de uma mesa de 3 eixos usada para validação de algoritmos embarcados de estimação e controle de atitude de satélites. Medidas vetoriais foram utilizadas para estimar atitude e velocidade angular e, adicionalmente, uma pseudo-medida baseada na derivada temporal filtrada das medidas vetoriais foi proposta para melhorar o desempenho do filtro. Filtros de Kalman estendido e unscented convencionais e um filtro de partículas comum foram comparados com a abordagem Rao-Blackwell aqui proposta para avaliar a acurácia da estimação de atitude e velocidade angular, a carga computacional, a taxa de convergência quando existem incertezas nas condições iniciais e a sensibilidade a distúrbios. Embora, no passado, já tenham sido propostos uma grande variedade de filtros para resolver o problemas de estimação em 3 eixos de atitude e velocidade angular de satélites, no melhor conhecimento dos autores o presente filtro de partículas Rao-Blackwell é uma nova abordagem que reduz significativamente a carga computacional, provê uma taxa de convergência atraente e preserva, com sucesso, o desempenho apresentado pelo filtro de partículas comum quando submetido a distúrbios. ABSTRACTA Rao-Blackwellized particle filter has been designed and its performance investigated in a simulated three-axis satellite testbed used for evaluating on-board attitude estimation and control algorithms. Vector measurements have been used to estimate attitude and angular rate and, additionally, a pseudomeasurement based on a low-pass filtered time-derivative of the vector measurements has been proposed to improve the filter performance. Conventional extended and unscented Kalman filters, and standard particle filtering have been compared with the proposed approach to gauge its performance regarding attitude and angular rate estimation accuracy, computational workload, convergence rate under uncertain initial conditions, and sensitivity to disturbances. Though a myriad of filters have been proposed in the past to tackle the problem of spacecraft attitude and angular rate estimation with vector observations, to the best knowledge of the authors the present Rao-Blackwellized particle filter is a novel approach that significantly reduces the computational load, provides an attractive convergence rate, and successfully preserves the performance of the standard particle filter when subjected to disturbances.

Hybrid Attitude Control for Nano-Spacecraft: Reaction Wheel Failure and Singularity Handling

“…Values for σ q and S ω can be deduced from the literature in which various sensors and determination techniques have been used [17,27,28].…”

A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

“…A PF is a sequential Monte Carlo method (SMC)[ 34 ] aimed at estimating the state of a dynamical system subject to random perturbations, via noisy output observations. A particle filter solution represents an attractive choice for UAV attitude estimation problems [ 20 , 35 , 36 , 37 , 38 , 39 ] outperforming classical Kalman filter (KF)-based approaches [ 40 ] at the price of significant computational complexity growth [ 20 ]. A PF-based fault detection scheme is proposed by [ 41 ], while [ 42 ] highlights PF advantages in tackling FDI for non-linear systems and [ 43 ] also discusses PF drawbacks.…”