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.
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.
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