An autonomous celestial navigation method for lunar probe based on RJMCMC algorithm particle filter

Autonomous celestial navigation system is a typical nonlinear, non-Gaussian dynamic system. Because the extended Kalman filter method is not good at dealing with nonlinear and/or non-Gaussian problems and the conventional particle filter method exists particle degeneration problem. A new autonomous celestial navigation method for lunar probe based on RJMCMC (Reversible-Jump Markov Chain Monte Carlo) method is presented in this paper. A simulation result demonstrated the validity and feasibility of this new method in quickening up the speed of convergence, increasing the precision of position determination and adjusting the sample size automatically.