Firefly Algorithm Optimized Particle Filter for Relative Navigation of Non-cooperative Target

Dali Zhang, Chao Zhong, Changhong Wang, Haowei Guan, Hongwei Xia

Abstract: Particle filter (PF) has been proved to be an effective tool in solving relative navigation problems. However, the sample impoverishment problem caused by resampling is the main disadvantage of PF, which strongly affect the accuracy of navigation. To solve this problem, an improved PF based on firefly algorithm (FA) is proposed. Combine with the operation mechanism of PF, the optimization mode of FA is revised, and a new update formula of attractiveness is designed. By means of firefly group’s mechanism of survival of the fittest and individual firefly’s attraction and movement behaviors, this algorithm enables the particles to move toward the high likelihood region. Thus, the number of meaningful particles can be increased, and the particles can approximate the true state of the target more accurately. Simulation results show that the improved algorithm improves the navigation accuracy and reduces the quantity of the particles required by the prediction of state value.