Social-spider optimised particle filtering for tracking of targets with discontinuous measurement data
The particle filter (PF), a non-parametric implementation of the Bayes filter, is commonly used to estimate the state of a dynamic non-linear non-Gaussian system. The key idea is to construct a posterior probability satisfying a set of hypotheses representing a potential state of the system. Despite PF's successful applications, it suffers from sample impoverishment in real-world applications. Most of the recent PF-based techniques attempt to improve the functionality of the PF through evolutionary algorithms in the cases of unexpected changes in the system states. However, they have not addressed the discontinuity of observations which is unpreventable in the real world. This study incorporates a recently developed social-spider optimisation technique into PF to overcome the drawback of previous methods in these cases. To avoid premature degeneracy, evolutionary search extends the particle search space when observation is unavailable. The social-spider inspired proposal distribution and the corresponding particle weights are derived to approximate real model states. The experimental results show that the proposed method has superior performance in relation to other evolutionary PF in cases of large changes or discontinuous observations.