Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (4): 1235-1246.

### Target Identity Recognition Method based on Trusted Information Fusion

Lu Wanga, b, Chenglin Wena, c, d, *, and Lan Wud

1. a Department of Electrical Automation, Shanghai Maritime University, Shanghai, 201306, China;
b Department of Image and Network Investigation, Railway Police College, Zhengzhou, 450053, China;
c Institute of Systems Science and Control Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China;
d College of Electrical Engineering, Henan University of Technology, Zhengzhou, 450001, China
•  Revised on  ; Accepted on
• Contact: E-mail address: mailw@rpc.edu.cn
• About author:Lu Wang is currently working toward his Ph.D. in the Department of Electrical Automation at Shanghai Maritime University. His research interests include reliability assessment and control and intelligent information processing. Chenglin Wen is currently a professor and chair with the Institute of Systems Science and Control Engineering in the School of Automation at Hangzhou Dianzi University. His current research interests include multi-sensor networked information fusion theory, multi-target racking, fault diagnosis of complex systems and devices, reliability assessment and health control, recognition, and tracking of hypersonic vehicles. He is currently a committee member of the Intelligent Automation Committee and Process Fault Diagnosis and Security Committee of Chinese Association of Automation. Lan Wu is currently a professor in the College of Electrical Engineering at Henan University of Technology. Her research interests include system modeling, information fusion technology, and nonlinear control.

Abstract: Safe and reliable target identity recognition is the important foundation of information security. In the complex environment of multi-source target information, in view of the potential impact of many uncertain factors on target identity recognition and the performance requirements of information security in the process of recognition, a trusted target identity recognition method is proposed in this paper. The BP neural network based on momentum factor is used to study and build an ensemble classification model, and based on this model, the trusted target identity recognition model is constructed. According to the relevant information characterized by the model, it can improve the recognition reliability of the target to a certain extent, thus providing more security and credibility for the recognition of the identified target. Finally, the effectiveness and feasibility of the proposed algorithm is verified by simulation experiments under an uncertain set environment.