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中文摘要：
将蚁群算法与遗传算法相结合,提出一种快速实现多目标数据关联的AC-GADA（ant colony-genetic algorithm data association）算法,该算法利用种群个体携带信息素,并改进了全局信息素扩散模型,同时为了提高算法的收敛速度并且避免局部极值的出现,引入了交叉变异策略和种群适应度模型,通过大量的实验数据证明,该算法在获得较高关联准确率的同时可以有效地提高关联速度。
英文摘要：
An AC-GADA （ant colony-genetic algorithm data association） algorithm was proposed to deal with the data association problem for multi-target tracking. This algorithm designed difference pheromone for each ant and improved global pheromone increment model, and combined crossover and mutation strategy with fimess of population model in order to improve rate of convergence and avoid the appearance of local extremum. The comparison with ACDA （ant colony data association） and JPAD （joint pobabilistic data association） proved its efficiency and superiority.
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