摘要
基于全过程综合敌我识别中不同阶段综合敌我识别信息来源的差异,采用动态贝叶斯网络进行建模.在建模过程中,由于参数众多、样本难以全面获得、学习训练计算量巨大等问题,将随机模糊思想引入参数学习,从而既可充分利用先验信息,又尽可能地消除主观因素.最后仿真了整个过程,其结果验证了所提出方法的有效性.
Based on the difference of information source during different identification phases, dynamic Bayesian network is used to model the whole process of integrated identification friend-or-foe. Due to the increasing number of model parameters, the acquiring of multitudinous swatch and the learning and training process become difficult. Therefore, the random fuzzy theory is adopted for parameter learning, which not only makes sufficient use of transcendent information, but also avoids the subjective factor as utmost as possible. The simulation results show the effectiveness of the proposed method.
出处
《控制与决策》
EI
CSCD
北大核心
2011年第3期443-447,共5页
Control and Decision
关键词
融合识别
贝叶斯网络
随机模糊
fusion identification
Bayesian network
random fuzzy
