| Study on the Hungarian algorithm for the maximum likelihood data association problem |
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| 摘 要： | A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood data association problem is formulated as a bipartite weighted matching problem. Its duality and the optimality conditions are given. The Hungarian algorithm with its computational steps, data structure and computational complexity is presented. The two implementation versions, Hungarian forest （HF） algorithm and Hungarian tree （HT） algorithm, and their combination with the naYve auction initialization are discussed. The computational results show that HT algorithm is slightly faster than HF algorithm and they are both superior to the classic Munkres algorithm.
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| 关 键 词： | 最大似然法 数据相关问题 匈牙利算法 线性规划 多目标跟踪 |
| 收稿时间： | 30 September 2005 |
Study on the Hungarian algorithm for the maximum likelihood data association problem |
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| Authors: | Wang Jianguo He Peikun Cao Wei |
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| Institution: | 1. The Second Academy of China Aerospace Science & Industry Corp., Beijing 100854, P. R. China
2. Dept. of Electronic Engineering, Beijing Inst. of Technology, Beijing 100081, P. R. China |
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| Abstract: | A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood data association problem is formulated as a bipartite weighted matching problem. Its duality and the optimality conditions are given. The Hungarian algorithm with its computational steps, data structure and computational complexity is presented. The two implementation versions, Hungarian forest (HF) algorithm and Hungarian tree (HT) algorithm, and their combination with the na(i)ve auction initialization are discussed. The computational results show that HT algorithm is slightly faster than HF algorithm and they are both superior to the classic Munkres algorithm. |
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| Keywords: | Tracking Data association Linear programming Hungarian algorithm |
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