Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9543
Title: Case study and proofs of ant colony optimisation improved particle filter algorithm
Authors: Zhong, J
Fung, Y
Issue Date: 2012
Publisher: Inst Engineering Technology-Iet
Source: IET control theory and applications, 2012, v. 6, no. 5, p. 689-697 How to cite?
Journal: IET Control Theory and Applications 
Abstract: 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 (PF ACO), 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 PF ACO 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 PF ACO 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.
URI: http://hdl.handle.net/10397/9543
DOI: 10.1049/iet-cta.2010.0405
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