Optimization algorithm for distributed target detection integration decision

被引：1
作者：
Hu, Xue-Hai [1 ]
Wang, Hou-Jun [1 ]
Huang, Jian-Guo [1 ]
机构：
[1] School of Automation Engineering, University of Electronic Science and Technology of China
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2013年 / 42卷 / 03期
关键词：
Bayes theory; Distributed detection; Distributed fusion; Sensor; Target identification;
D O I：
10.3969/j.issn.1001-0548.2013.03.011
中图分类号：
学科分类号：
摘要：
Existing distributed object detection systems generally use the enumeration method, SFFO method or SOFF method. These methods are computationally complex, and their optimization results strongly depend on initial value. A climbing variation ant colony algorithm is proposed in this paper. The algorithm is used to simultaneous optimization of sensor decision threshold and the fusion center decision rules. The numerical results show that the fusion system reduces Bayes risk about 15%~20%, the optimization results do not depend initial value, and the computational complexity is lower than the other algorithms.
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页码：375 / 379
页数：4
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