Abstract:

Degeneracy phenomenon and large calculation are the main disadvantages of particle filtering, which restricts its application. Common resampling methods can resolve the degeneracy phenomenon, but the sample impoverishment and large calculation is a secondary result. So a mean approaching particle filter based on chaotic perturbation is proposed to resolve the above problem. The particles are divided into two groups according to its weight, and then the small weight particles are replaced by the mean so that the particles can approach from the low likelihood region to the high likelihood region. Moreover, the difference between the particle distribution produced by the behavior of mean approaching and the likelihood distribution is described by the Kullback information. The Kullback information decreases with the increasing iteration degree, which proves that this algorithm is rational. Furthermore, a chaotic perturbation resampling method is presented to reduce calculation, which imports the chaotic variable that has the property of stochastics similar to the carrier wave in order to ameliorate the diversity of samples. In addition , this algorithm is applied to a certain type of missile attitude estimation. The simulation results demonstrate that the new algorithm is feasible.

Key words: attitude estimation;particle filter;mean approaching;chaotic perturbation;simulation