The Evaluation of Effectiveness for the Collaborative Combat of an Unmanned Aerial Vehicle Based on Grey Minimum Entropy

ABSTRACT The main points to the evaluation of effectiveness for the collaborative combat of the unmanned aerial vehicle (UAV) lie within the construction of a reasonable indicator system and an accurate contribution model. As for point one, this article introduces a new method combining the Delphi consulting method and the principal component analysis method to avoid the underlying subjective and time-consuming defects of the existing methods. As for another point, a weighting method is adopted combining the subjective and objective parameters to minimize the errors caused by a single entity. Firstly, the modified grey relational degree analysis method is used to obtain the subjective weight, which can reduce the influence of the extreme values and outliers by enhancing the selection process of the reference sequence. Secondly, this paper adopts the weight of minimum entropy weight method to obtain the objective weight; it can avoid the information loss caused by the original method, which only determines the weight based on the frequency of each element present in the effective combination. At last, the principle of minimum relative entropy is adopted to obtain a more reasonable weight distribution coefficient. The simulation experiments established the rationality and effectiveness of the proposed method.

摘要
无人机（Unmanned Aerial Vehicle，UAV）协同作战效能评估的核心要点在于构建合理的指标体系与精准的贡献度模型。针对指标体系构建环节，本文提出一种结合德尔菲咨询法（Delphi Consulting Method）与主成分分析法（Principal Component Analysis）的新方法，以规避现有方法固有的主观性强、耗时久的缺陷。针对权重赋值环节，本文采用融合主观与客观参数的权重分配方法，以最小化单一主体带来的误差：首先，采用改进型灰色关联度分析法（Grey Relational Degree Analysis）获取主观权重，该方法通过优化参考序列的遴选流程，可降低极端值与异常值的影响；其次，采用最小熵权法（Minimum Entropy Weight Method）获取客观权重，该方法可规避传统方法仅依据有效组合中各元素的出现频率确定权重所导致的信息损失问题；最后，采用最小相对熵原理（Minimum Relative Entropy）得到更为合理的权重分配系数。仿真实验验证了所提方法的合理性与有效性。