Group Targets Tracking Using Maximum Entropy Fuzzy Based on Fire-fly Algorithm and Particle Filter

In the complex tracking scenes, such as the formation of the aircrafts and the battleships, the ground moving tanks, the ballistic missiles and so on, the tracked objects are formed by targets moving in an interacting fashion which shows the characteristics of the group. The research on group targets tracking plays an important role in theoretical significance and military value. In order to solve the problem of complex data correlation and particle degradation, a novel algorithm, called fire-fly algorithm and particle filter (FA-PF), is proposed based on maximum entropy fuzzy (MEF) for fine tracking of group targets. The FA-PF algorithm can estimate not only the uncertainty of the group structure but also the group target states. The MEF clustering data association algorithm is used to improve the accuracy and efficiency of data association. The improved intelligent fire-fly algorithm is combined with the particle filter to improve the tracking accuracy. Therefore, the proposed algorithm can reduce the complexity of data association and improve the accuracy and efficiency of the group targets tracking compared with the existing fine tracking algorithm of group targets. The simulation results verified the effectiveness of the proposed algorithm.