A Diffusion Model-Based Open Set Identification Method for Specific Emitters
Wenyan Wang, Zheng Dou, Jiangzhi Fu, Yun Lin · 2023
This paper proposes an open set identification architecture for Specific Emitters, which consists of four modules: a diffusion module, a denoising module, an out-of-distribution (OOD) detection module, and a classifier. The diffusion module destroys the input signal into a Gaussian prior distribution, while the denoising module restores the corresponding Gaussian distribution to the original data. The OOD detection module evaluates whether a sample belongs to a known class, and a classifier is used to identify emitters within the known class. Experimental results on the ADS-B dataset demonstrate that the proposed method outperforms the OpenMax algorithm with a 0.09 improvement in macro-F1 score at 13.4% openness. These results show that the proposed method is a promising solution for SEI in the open set scenario.