极小子样下无人侦察机系统效能评估多层Bayes-ADC模型

针对研发阶段无人侦察机系统效能评估受到研发试验成本、样机数量等相关条件制约,其所能获取的样本量极少问题,提出多层Bayes-ADC模型.该方法运用多层Bayes技术,充分利用历史统计数据和经验信息,确定无人侦察机失效率、修复率多层Bayes估计,进而构造无人侦察机可用度、可信度.同时基于极大熵准则确定能力指标权重,进而构造无人侦察机能力向量.基于以上建立了极小子样背景的系统效能评估多层Bayes-ADC模型,解决了极小子样下的无人侦察机系统效能评估问题.案例分析验证了模型的有效性.

Multilayer Bayes-ADC model for system effectiveness evaluation of UAV with minimum sample

The multilayer Bayes-ADC model was proposed to solve the problem that the effectiveness evaluation of the unmanned reconnaissance system in the development phase is limited by development test cost and the number of prototypes, the sample size that can be obtained is extremely small. The model uses multi-layer Bayes to determine the multilayer Bayes estimation of failure rate and repair rate of UAV by making full use of historical statistical data and empirical information, and then construct the availability and credibility of UAV. At the same time, based on the maximum entropy criterion, the weight of the capability index is determined, and then UAV's capability vector is constructed. Based on the above, the multilayer Bayes-ADC model for system effectiveness evaluation is established with minimum sample, and the problem of the system effectiveness evaluation of UAV is solved with minimum sample. The case analysis verifies the validity of the model.