.png){kind=icon}
.png){kind=icon}
Return
Sequential Filtering Techniques for Simultaneous Tracking and Parameter Estimation
.png){kind=icon}
OA
AI
DOI：10.1016/j.ast.2026.111951.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• A stochastic formulation of orbital dynamics based on Itô stochastic differential equations is introduced to model unresolved perturbations in orbit determination. • Three hybrid sequential Monte Carlo filtering algorithms are proposed for simultaneous spacecraft state tracking and online estimation of process-noise parameters. • The ensemble Kalman filter and particle filter are extended to jointly infer diffusion coefficients governing propagation uncertainty. • Extensive Monte Carlo simulations in LEO and GEO scenarios demonstrate improved uncertainty realism and competitive accuracy–cost trade-offs. • The proposed framework enables robust tracking with simplified dynamical models while maintaining estimation consistency.
Keywords:
Tracking
sequential filters
particle filters
SMC
parameter estimation
uncertainty propagation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.8
Papers:
1.0W
Citations:
3.0W