基于自适应遗传算法的航迹关联模型

Track Correlation Model Base on Adaptive Genetic Algorithm

摘要:

如何解决关联过程中产生的多维分配问题,是研究多节点航迹关联的关键。遗传算法能够很好地解决组合优化问题。但是基本遗传算法具有收敛速度慢和易于早熟等缺点,对于计算航迹关联问题来说实时性较差。文章在利用遗传算法解决航迹关联问题算法的基础上进行了改进,提出了自适应策略。仿真试验表明,该模型能够使航迹关联保持较高的正确关联率和较快的计算速度。

Abstract:

It is important that how to solve the multi-dimension assignment in research of multi-sensor track correlation. Genetic Algorithm (GA) is good at solving combination optimization problem. But the Simple GA has the disadvantage of reaching solution slowly and precociousness that makes against the real time of computing track correlation. So the track correlation model base on SGA was improved. Some self-adaptation strategy was put forward. The experimental results illustrated that this model could keep the track correlation have higher correct association percent and quicker computing rate.