分布式目标检测融合决策优化算法

Optimization Algorithm for Distributed Target Detection Integration Decision

现有分布式目标检测系统的优化一般采用穷举法、SFFO算法或SOFF算法,计算复杂,且优化结果强烈依赖初值。该文采用蚁群算法和爬山变异算法结合,提出爬山变异蚁群算法及同步优化传感器判决门限和融合中心决策规则,理论上是一种全局最优算法。数值试验的结果表明,和相关算法相比,融合系统的贝叶斯风险降低了15%～20%,且优化结果不依赖初值,计算复杂度低于其他算法。

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.