基于改进蚁群算法的多目标跟踪数据关联方法

摘要

For the application of multi-sensor multi-target tracking, a method of data association based on improved ant colony algorithm is proposed in this study, in order to improve the ant colony algorithm in which the application effect of global optimization problems, the initial solution is built and optimized by use of the characters of positive feedback and parallel search of ant colony algorithm, introducing an adaptive Chaos mechanism, globally pheromone update and chaotic disturbance. Experimental results show that the presented algorithm is effective.%针对多目标跟踪数据关联问题，提出一种快速实现多目标数据关联算法CACDA（Chaos Ant Colony Data Association），利用蚁群算法的正反馈和并行搜索能力构建初始解并进行优化，引入自适应混沌机制，对信息素进行全局更新和混沌扰动，改善了蚁群算法在搜索后期出现停滞以及收敛于局部最优解的缺陷。实验结果表明，该算法不仅可以获得较高的关联准确率，也可以有效提高关联速度。