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Hybrid Adaptive Particle Swarm Optimized Particle Filter for Integrated Navigation System

Zhimin Chen1,2, Yuanxin Qu1, Tongshuang Zhang1, Xiaoshu Bai1, Xiaohong Tao1, Yong Liu1
China Satellite Maritime Tracking and Controlling Department, Jiangyin, 214431, China.
Corresponding author. E-mail:

Computer Modeling in Engineering & Sciences 2015, 106(6), 379-393.


Particle swarm optimization algorithm based particle filter is trapping in local optimum easily, it is not able to satisfy the requirement of modern integrated navigation system. In order to solve the problem, A novel particle filter algorithm based on hybrid adaptive particle swarm optimization(HPSO-PF) is presented in this paper. This improved particle filter will conduce to finding the ideal solution domain by making use of the global convergence of artificial fish swarm and enhancement of fusion precision by guiding particles to move toward the high likelihood area through particle swarm optimization. Finally different models are used for simulation and the experiment results show that this new particle filter improves the precision of integrated navigation system.


dynamic, particle filter, integrated navigation, hybrid adaptive

Cite This Article

Chen, Z., Qu, Y., Zhang, T., Bai, X., Tao, X. et al. (2015). Hybrid Adaptive Particle Swarm Optimized Particle Filter for Integrated Navigation System. CMES-Computer Modeling in Engineering & Sciences, 106(6), 379–393.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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