Hummingbirds optimization algorithm-based particle filter for maneuvering target tracking
As a commonly used filtering method for nonlinear non-Gaussian systems, particle filters (PFs) have been successfully applied in the field of maneuvering target tracking. However, particle impoverishment is a major obstacle to the PF performance. To overcome this defect, this paper combines the hummingbirds optimization algorithm (HOA) with a standard PF and proposes an HOA-based PF (HOA-PF) for maneuvering target tracking. The proposed filter treats the particles as individual hummingbirds, simulates the honey-collecting process of hummingbirds in nature and moves the particles as a whole to the high-likelihood region by performing self-searching and guided-searching phases. Moreover, to enhance the particle diversity, the mutation method of the following birds in the HOA is improved. Thus, the distribution of particles in the HOA-PF is reasonable. The results of experiments on the univariate nonstationary growth model and the maneuvering target tracking problem demonstrate the effectiveness of the proposed method.
KeywordsParticle filter Particle impoverishment Hummingbirds optimization algorithm Maneuvering target tracking
This work is supported by the National Natural Science Foundation of China under Grant No. 61601505.
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Conflict of interest
The authors declare that they have no conflicts of interest to disclose.
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