Case study and proofs of ant colony optimisation improved particle filter algorithm
Junpei Zhong, Y.F. Fung · IET Control Theory and Applications · 2012
Particle filters (PF), as a kind of non-linear/non-Gaussian estimation method, are suffering from two problems in large-dimensional cases, namely particle impoverishment and sample size dependency. Previous studies from the authors have proposed a novel PF algorithm that incorporates ant colony optimisation (PFACO), to alleviate these problems. In this paper the authors will provide a theoretical foundation of this new algorithm; two theorems are introduced to validate that the PFACO introduces smaller Kullback–Leibler divergence (K–L divergence) between the proposal distribution and the optimal one compared to those produced by the generic PF. In addition, with the same threshold level, the PFACO has a higher probability than the generic PF to achieve a certain K–L divergence. A mobile robot localisation experiment is applied to examine the performance between various PF schemes.