Attitude Motion Estimation based on Particle Swarm Optimization by SLR Data

抄録

In a previous study, we proposed a method for attitude motion estimation from SLR data using a genetic algorithm (GA). While it is a very effective method in that it requires no prior information, it has the disadvantage of inefficient local solution search. Therefore, we propose a new estimation method using another global optimization method, particle swarm optimization (PSO). In this paper, we performed numerical simulations with both the PSO and the GA for the same cases as in the previous study, and compared the obtained results. As a result, followings are revealed: (1) Good solutions were obtained by the PSO as well as the GA even if observations are polluted by noise and bias and the rotational speed is slow. (2) The PSO tends to find solutions closer to the true value than the GA if there is no bias in the observation times. (3) It is preferable to use the PSO in the problem setting assumed in the paper. We also describe a preprocessing to perform the analysis using actual SLR data.