| Abstrakt: |
This paper presents a signal generation architecture for simulating arbitrarily intercepted low-probability-of-intercept (LPI) signals in a battlefield environment. Additionally, the performances of deep learning models in classifying the generated signals are compared in terms of the classification time, GPU memory usage, and accuracy. Previous studies have utilized ensemble learning to minimize signal classification time and enhance accuracy; however, an explicit criteria for the adoption of deep learning models have been lacking. This paper presents the analysis of the performances of 11 deep learning models on the basis of simulation results and proposes an ensemble model that utilizes MobileNet V3 Small as the main model and Densenet-169 as the sub-model. [ABSTRACT FROM AUTHOR] |
