MCMC for Sequential Flight Object Attitude Estimation Based on Perfect Coupling Sampling

Aiming at large initial attitude errors of flight object, this paper presents perfect coupling sampling based on coupling from the past (CFTP) algorithm on MCMC (Markov chain Monte Carlo) to tackle the problem of sequential flight object attitude estimation. Based Bayesian theory, posterior distribution can be approximated by Monte Carlo likelihood function and conjunction prior distribution based via constructing MCMC of flight object monotonous state-space and difference encoding. It is so-called perfect-sampling of MCMC method, which can guarantee that samples are drawn exactly from distribution of flight object attitude estimation. Simulation results show that this method can reduce computing complexity and effectively explore the time of convergence of sequential flight object attitude estimation.