数据缺失下基于IOWA TOPSIS的辐射源威胁评估

由于战场环境的高复杂性,侦察方难于获取目标辐射源的完备信息.为解决传统辐射源威胁评估算法不适用于数据缺失情况的问题,引入诱导有序加权平均算子空值估计算法,与逼近理想解排序法相结合,采用CV-G1法赋权,构建数据缺失下基于IOWA-TOPSIS的辐射源威胁评估模型.首先,利用IOWA算子估算空值,解决数据缺失问题;然后,利用基于变异系数法改进的G1法实现对各属性的组合赋权;最后,通过TOPSIS算法对辐射源威胁度进行排序.仿真验证了算法的有效性,该方法拓展了TOPSIS算法使用范围,实现数据缺失情况下的辐射源威胁评估.

A Radiator Threat Assessment Based on IOWA TOPSIS under Conditions of Missing Data

Aimed at the problems that battlefield environments are complex, reconnaissance party is difficult to obtain complete information of target emitter, and the traditional radiation source threat assessment algorithms are not suitable for the case of data loss, an Induction Ordered Weighted Average operator null value estimation algorithm is introduced in combination with Technique for Order Preference by similarity to an Ideal Solution method weighted by CV correction G1 method to construct the radiator threat assessment model based on IOWA TOPSIS under conditions of missing data. Firstly, IOWA operator is utilized for estimating the null value to solve the problem of incomplete data. Then, the improved G1 method based on coefficient of variation method is used to realize the combination weight of each attribute. Finally, the threat degree of radiation sources is sorted by TOPSIS algorithm. The effectiveness of the algorithm is verified by simulation. The method expands the application range of TOPSIS algorithm, and realizes the emitter threat assessment in the case of missing data.