A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing

Electronics

Jundi Wang¹  Xing Wang¹  You Chen¹  Zhenkun Chen¹  Yuanrong Tian² 

[1] Aviation Engineering School, Air Force Engineering University, 1 Baling Road, Xi’an 710038, China;School of Electronic Countermeasure, National University of Defense Technology, Hefei 230037, China;

关键词: warning device;    emitter identification;    hierarchical processing;    IFS multi-attribute decision-making;    tri-training;    cognitive electronic warfare;   

DOI  :  10.3390/electronics11071078

来源: DOAJ

【 摘 要 】

Radar Warning Receiver (RWR) is one of the basic pieces of combat equipment necessary for the electromagnetic situational awareness of aircraft in modern operations and requires good rapid performance and accuracy. This paper proposes a data processing flow for radar warning devices based on a hierarchical processing mechanism to address the issue of existing algorithms’ inability to balance real-time and accuracy. In the front-level information processing module, multi-attribute decision-making under intuitionistic fuzzy information (IFS) is used to process radar signals with certain prior knowledge to achieve rapid performance. In the post-level information processing module, an improved tri-training method is used to ensure accurate recognition of signals with low pre-level recognition accuracy. To improve the performance of tri-training in identifying radar emitters, the original algorithm is combined with the modified Hyperbolic Tangent Weight (MHTW) to address the problem of data imbalance in the radar identification problem. Simultaneously, cross entropy is employed to enhance the sample selection mechanism, allowing the algorithm to converge rapidly.

【 授权许可】

Unknown