State Estimation and Quick Trajectory Optimization for Air-Launch Rocket

“Air Launch Rocket” is a promising launch system for small satellites. Air-launch rockets have potential to achieve low-cost and responsive missions. One of the biggest technical problems in air-launching is that the attitude of the separated rocket is very unstable and it is hard to predict its motion. The attitude motion and the angle at the ignition can stray far from the nominal. Therefore it is necessary to estimate the motion of the separated rocket and revise the trajectory program quickly to achieve reliable air-launching. Then I suggest taking approaches using the probability methods to these problems. In this research, particle filter was used for estimation and MCMC (Markov Chain Monte Carlo) method was used for optimization. These methods are new powerful techniques developed in mathematical statistics. This paper shows numerical simulation results of state estimation and trajectory optimization for air-launch rocket using these new methods.