目标类型识别的改进灰关联模型

针对防空作战目标类型识别的具体需求,分析了传统和广义灰关联模型在处理数据时存在的不足之处,建立了基于熵权法的改进灰关联目标类型识别模型。首先,为了提高空情数据的利用率,运用熵权法对数据熵值进行描述而后客观地赋予权重;其次,用置信度取代数据的绝对差值,更加准确地描述数据间相对差异的偏差情况;最后,对模型输出结果进行离散化处理,增强了模型的区分能力。用实例对模型进行检验,结果表明:该模型准确、简单且识别率高。

Air Target Type Recognition Model Based on Improved Grey Relation Theory

For the requirement of Target type recognition in air defense combat process, this paper points out the limitations of traditional and generalized Grey Relation (GR) models in data processing, and then an improved GR model based on entropy-weight method to recognize the unknown target type is established. Firstly, in order to make full use of air targets'' data exactly, the method of entropy-weight is introduced in this model to determine the index weight. Secondly, the Certainty Factor is used instead of the absolute value to more accurately describe data errors between different serial numbers. Finally, the discretization of the output of this model is made, which enhances the ability to distinguish different target types effectively. Furthermore, examples are taken to validate this model, the results show that the model is accurate, simple, high in recognition rate and of great value in application.