不完备信息下基于切换推理证据网络的空中目标识别方法

Aerial target identification method based on switching reasoning evidential network under incomplete information

现有证据推理方法模型结构固定、信息处理方式和推理机制单一,难以适用于集结了不确定、错误甚至缺失等多种不完备信息环境下的目标识别。针对该问题,提出了一种切换推理证据网络(SR-EN)方法。首先,考虑证据节点删除等情况构建多模板网络模型;然后,分析各证据变量与目标类型的条件关联性以建立针对不完备信息的推理规则库;最后,提出基于三种证据输入及修正方式的智能化时空融合推理方法。与传统的证据网络(EN)以及EN与优劣解距离法(TOPSIS)等两种信息修正方法的结合方法相比,SR-EN能够在确保推理时效性的同时实现在多类随机性不完备信息下对空中目标的连续准确识别。实验结果表明,通过对各类不完备信息的有效识别,SR-EN能够实现连续推理过程中证据处理方式、网络结构和节点间融合规则的自适应切换。

Existing evidential reasoning methods have fixed model structure,single information processing mode and reasoning mechanism,making these methods difficult to be applied to target identification in an environment with a variety of incomplete information such as uncertain,error and missing information.To address this problem,a Switching Reasoning Evidential Network(SR-EN)method was proposed.Firstly,a multi-template network model was constructed considering evidence-node deletion and other situations.Then,conditional correlation between each evidence variable and target type was analyzed to establish an reasoning rule base for incomplete information.Finally,an intelligent spatio-temporal fusion reasoning method based on three evidence input and correction methods was proposed.Compared with traditional Evidential Network(EN)and combination methods of two information correction methods,such as EN and Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS),SR-EN can achieve continuous and accurate identification for aerial targets under multiple types of random incomplete information while ensuring reasoning timeliness.Experimental results show that SR-EN can realize adaptive switching of evidence processing methods,network structures and fusion rules among nodes in continuous reasoning process through effective identification of various types of incomplete information.