Multi-Feature Fusion Classification Approach for Specific Communication Emitter Identification
Zitao Tong, Tao Zhang, Hao Wu, Yihang Du, Xiaoqiang Qiao, Jiang Zhang · 2024
Addressing issues such as the limited focus on features and poor interpretability in deep learning models for the individual recognition of communication emitters, this study introduces a multi-domain feature fusion model. Initially, wavelet transforms are applied to raw signals to generate two-dimensional time-frequency images. Subsequently, features are extracted from these images using neural networks to form one-dimensional image feature vectors. Additionally, multi-domain parameters of the signals are manually extracted. These two types of feature vectors are concatenated to create a combined feature set. Simulation results indicate that, on the dataset of Automatic Dependent Surveillance-Broadcast (ADS-B) emitters from multiple aircraft, selecting 10 categories of radiation sources, the proposed method improves identification accuracy by approximately 15% compared to using image features alone, achieving up to 97%. This enhancement significantly improves the accuracy of individual communication transmitter identification.