Rao-Blackwellized Particle Filter for the CBERS-4 attitude and gyros bias estimation

Silva, William Reis; Garcia, Roberta Veloso; Santilli, Giancarlo; Kuga, Helio Koiti; Zanardi, Maria Cecília; Pardal, Paula Cristiane Pinto Mesquita

doi:10.1016/j.actaastro.2021.07.049

Cited by 5 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The standard PF was implemented using re-sampling in a systematic way to increase particle diversity [3,30] with the intention of evaluating and measuring the performance of the proposed EH ∞ PF. More details about this estimation method and implementation can be found in [1,20,31]. The mean error, root mean square error (RMSE), and processing time cost of the estimation methods used were analyzed and compared.…”

Section: Methodsmentioning

confidence: 99%

“…), which is obtained intrinsically by contributing the measurement model h(.) and the noise measurements υ υ υ k , represented by [3,19,20,27].…”

Section: Update Stepmentioning

confidence: 99%

“…The EH ∞ PF proposed here uses N particles with different values analyzing a possible sample impoverishment, a common problem for PF under the same conditions [3,20]. The idea is to analyze the processing time and the accuracy of the method.…”

Section: S S Smentioning

confidence: 99%

“…The PROPAT functions perform coordinate transformations, time and ephemeris conversion, orbital propagation, attitude transformations in various parameterizations, and attitude propagation [11]. The initial orbit and attitude conditions can be found in detail in [20]. In this article, the scope of the problem is expanded using a robust estimation method.…”

Section: Parametersmentioning

confidence: 99%

“…[1] makes a comparison between the estimation methods of UKF, regularized particle filter (RegPF), and extended H ∞ filter (EH ∞ F); and ref. [20] presents the Rao-Blackwellized particle filter (RaoBPF) attitude estimation and gyros bias for the CBERS 4 satellite, evaluating computational processing time cost and precision.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The Extended H∞ Particle Filter for Attitude Estimation Applied to Remote Sensing Satellite CBERS-4

Silva

Garcia

Pardal³

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

An extension of the linear H∞ filter, presented here as the extended H∞ particle filter (EH∞PF), is used in this work for attitude estimation, which presents a process and measurement model with nonlinear functions. The simulations implemented use orbit and attitude data from CBERS-4 (China–Brazil Earth Resources Satellite-4), making use of the robustness characteristics of the H∞ filter. The CBERS-4 is the fifth satellite of an advantageous international scientific interaction between Brazil and China for the development of remote sensing satellites used for strategic application in monitoring water resources and controlling deforestation in the Legal Amazon. In the extended H∞ particle filter (EH∞PF) the nature of the system, composed of dynamics and noises, seeks to degrade the state estimate. The EH∞PF deals with this by aiming for robustness, using a performance parameter in its cost function, in addition to presenting an advantageous feature of using a reduced number of particles for state estimation. The justification for the application of this method is because the non-Gaussian uncertainties that appear in the attitude sensors impair the estimation process and the EH∞PF minimizes in signal estimation the worst effects of disturbance signals without a priori knowledge of them, as shown in the results, in addition to presenting good precision within the prescribed requirements, with 100 particles representing a processing time 2.09 times less than the PF with 500 particles.

show abstract

Section: Methodsmentioning

confidence: 99%

“…), which is obtained intrinsically by contributing the measurement model h(.) and the noise measurements υ υ υ k , represented by [3,19,20,27].…”

Section: Update Stepmentioning

confidence: 99%

Section: S S Smentioning

confidence: 99%

Section: Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%