Data Fusion Based on Hybrid Intelligent Optimization

摘要

To solve the problems of low precision, slow convergence speed and difficult data fusion of standard particle filtering algorithm, a new hybrid intelligent optimization algorithm applicable for data fusion is presented in this paper and 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 Gaussian area through particle swarm algorithm. Simulation shows that this algorithm can effectively break away from the local optimum, explore the idea particle optimal value and enhance the convergence speed and fusion precision.