Particle Filtering approach to parameter estimate and temperature prediction of satellite

To identify the heat flux dynamically and predict the temperature more correctly, a particle filtering (PF) algorithm based on a double lumped thermal model is put forward. In the PF approach to the dynamic state estimation, one attempts to construct the posterior probability density function of the state based on all available information including the set of received measurements. Because the PDF embodies all available statistical information, it is a more effective method for the nonlinear estimation and prediction problem studied in this paper. Simulations were conducted. Results demonstrated the algorithm based on the double lumped thermal model could meet the precision of dynamical identification and real-time prediction for a satellite. The algorithm has a greater potential to apply autonomous control and self-adapting manage of satellite thermal control system in the future.