Comparison of Optimizers for Drone Signal Detection Using Convolutional Neural Networks (CNN)

Abstract

Convolutional Neural Networks (CNNs) have emerged as a promising approach for drone signal detection from radio frequency (RF) data. The choice of optimizer significantly affects the performance of CNNs, as it influences the network's ability to learn complex patterns. In this paper, we conduct a comprehensive comparison of popular optimizers, including SGD, Adam, RMSprop, and Adagrad, and their variants, for drone signal detection using CNNs on an RF dataset. The results demonstrated that RMSprop outperformed other optimizers, particularly for larger datasets, maintaining consistently high accuracy with increased test data. Adam, Adamax, and AdamW also achieved impressive accuracy levels above 95%, with the exception of the Adadelta optimized model, which showed lower accuracy due to suboptimal performance during training. In conclusion, the RMSprop optimizer is the most suitable for drone signal detection using a CNN model, although other optimizers such as Adam, Adamax, and AdamW can also provide satisfactory results.