Multi-source Situation Information Fusion Based on Particle Swarm Optimization Evidence Theory
Yongwei Wang, Xiaoli Bian, Bin Wu, Huifang Su · 2019
According to the problems of existing situation information fusion algorithms in fusion efficiency, detection rate and false detection rate, a multi-source situation information fusion algorithm based on particle swarm optimization evidence theory is proposed. The algorithm combines the particle swarm optimization algorithm and evidence theory, uses the optimized particle swarm optimization algorithm to obtain the index weight of multi-evidence, and uses the simplified alarm as evidence fusion to improve the efficiency and accuracy of alarm fusion. Experimental results show that the proposed multi-source fusion algorithm has higher efficiency and lower false alarm rate, and its performance is better than the typical fusion methods.