CAJ | 학술논문

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。
침대다목표근종수거관련문제，제출일충쾌속실현다목표수거관련산법CACDA（Chaos Ant Colony Data Association），이용의군산법적정반궤화병행수색능력구건초시해병진행우화，인입자괄응혼돈궤제，대신식소진행전국경신화혼돈우동，개선료의군산법재수색후기출현정체이급수렴우국부최우해적결함。실험결과표명，해산법불부가이획득교고적관련준학솔，야가이유효제고관련속도。
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.