| 引用本文: | 徐丽,马培军,苏小红.基于K-Medoids聚类的多传感器航迹关联算法[J].哈尔滨工业大学学报,2012,44(1):107.DOI:10.11918/j.issn.0367-6234.2012.01.021 |
| XU Li,MA Pei-jun,SU Xiao-hong.A multi-sensor track association algorithm based on K-Medoids clustering[J].Journal of Harbin Institute of Technology,2012,44(1):107.DOI:10.11918/j.issn.0367-6234.2012.01.021 |
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| 摘要: |
| 为有效解决目标密集环境下的航迹关联问题,提出了一种基于K-Medoids聚类的航迹关联算法.该算法采用局部航迹与系统航迹进行关联的策略,将系统航迹作为Medoids,降低了需要关联的航迹对数量,避免了K-Medoids的固有缺陷,很大程度上提高了关联算法的效率.通过采用无穷范数计算采样点点迹距离求出了两条航迹的近似距离,这使得关联判决能考虑历史和当前航迹,提高了正确关联率.在多传感器多目标环境下讨论了其具体实现过程,仿真实验结果验证了该算法的有效性和优越性.该算法在存在噪音和离群点时,具有很强的健壮性,适合目标密集环境. |
| 关键词: 航迹关联 K-Medoids聚类 无穷范数 多传感器 多目标 |
| DOI：10.11918/j.issn.0367-6234.2012.01.021 |
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| 基金项目:国家自然科学基金资助项目(60773067);中央高校基本科研业务费专项资金资助项目(HEUCF100604) |
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| A multi-sensor track association algorithm based on K-Medoids clustering |
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XU Li1,2, MA Pei-jun1, SU Xiao-hong1
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1.School of Computer Science and Technology,Harbin Institute of Technology,150001 Harbin,China;2.College of Computer Science and Technology,Harbin Engineering University,150001 Harbin,China
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| Abstract: |
| To solve the problem of track association in dense target environment,a new track association algorithm based on K-Medoids clustering is presented.The algorithm makes the sensor tracks associate with the system tracks and selects the system tracks as Medoids,which greatly reduce the number of track pairs needed to associate,avoid the inherent defects of K-Medoids clustering,and improve the efficiency of the algorithm.In addition,the approximate distance between the two tracks is acquired by calculating the distance at a sampling point with the method of infinite norm,and then the track pair with minimum distance is selected as the associated tracks.The consideration of the historical and current track improves the probability of correct association.Finally,the algorithm is implemented in the multi-sensor and multi-target environment and the simulation results prove the effectiveness and superiority of the algorithm.The algorithm has a strong robustness in the presence of noise and outliers,and is suitable for the dense target environments. |
| Key words: track association K-Medoids clustering infinite norm multi-sensor multi-target |
