基于条件熵组合权法的无人机协同作战效能评估

IF 0.9 Q3 ENGINEERING, AEROSPACE Journal of Aerospace Technology and Management Pub Date : 2021-08-11 DOI:10.1590/jatm.v13.1227
Lifan Sun, Jiashun Chang, Jinjin Zhang, Zhumu Fu, Jie Zou
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引用次数: 0

摘要

在评估无人机协同作战效能时,传统的熵方法由于其高度分散性,使指标的权重系数过大,并且没有考虑指标之间的相互关系。针对这一问题,本文提出了一种评估无人机协同作战效能的条件熵组合加权方法。首先,为了建立无人机协同作战指标体系,将改进的德尔菲法与层次分析法(AHP)和区间估计相结合。该方法已用于估计每个指数的贡献程度,并用于去除贡献较低的指数。其次,引入条件熵的原理,考虑指标之间的相互关系,对熵法进行了修正。最后,将修正熵和AHP相结合,对无人机协同作战系统进行了最终权重分配。测试结果表明,与传统的德尔菲方法相比,该方法建立的指标体系更加全面合理。与单加权法相比,该方法更适合于无人机协同作战效能的评估体系。
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Evaluation of Unmanned Aerial Vehicles Cooperative Combat Effectiveness Based on Conditional Entropy Combination Weight Method
For evaluating the cooperative combat effectiveness of unmanned aerial vehicles (UAVs), traditional entropy methods have an undue weight coefficient of the index due to its high degree of dispersion, and the interrelationship between the indices are not considered. To deal with this problem, this paper proposes a conditional entropy combination weighting method for evaluating the cooperative combat effectiveness of UAVs. Firstly, with the aim of establishing the UAV cooperative combat index system, the modified Delphi method has been combined with analytic hierarchy process (AHP) and interval estimation. This method has been used for estimating the degree of contribution of each index and to remove the indices that have a low contribution. Secondly, the principle of conditional entropy has been introduced for modifying the entropy method with the consideration of the interrelation between the indices. Finally, the modified entropy and AHP have been combined to assign the final weight in the UAV cooperative combat system. Testing results demonstrate that the index system established by this method is more comprehensive and reasonable as compared to that established by the traditional Delphi method. Compared with the single weighted method, this method is more suitable for the evaluation system of UAVs cooperative combat effectiveness.
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CiteScore
2.00
自引率
0.00%
发文量
16
审稿时长
20 weeks
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