Problem
Research questions and friction points this paper is trying to address.
drone detection
radio-frequency emissions
compact neural networks
software-defined radio
embedded systems
Compact convolutional neural networks for AI-based drone detection system
🤖 AI Summary
This study addresses the growing concern of first-person-view (FPV) drone misuse in complex electromagnetic environments by proposing a lightweight and efficient radio-frequency signal detection method. The approach leverages software-defined radio to capture drone video transmission signals and directly converts them into time-domain raster images, bypassing conventional spectrogram generation and frequency-domain preprocessing. A compact convolutional neural network is designed to enable end-to-end detection. Experimental evaluation on a dataset of approximately 40,000 annotated images demonstrates that the model achieves high accuracy while significantly reducing computational overhead, exhibiting low latency and a small footprint. The system has been successfully deployed in a real-time monitoring platform on an embedded electronic warfare system.
📝 Abstract
The increasing use of first-person-view drones in modern conflicts has created a demand for compact and reliable detection systems capable of operating in complex electromagnetic environments. These drones continuously transmit video signals through onboard video transmitters, generating radio-frequency emissions that can be exploited for early detection. This study investigates the use of lightweight convolutional neural networks for automated detection of drone signals captured by a software-defined radio-based electronic warfare framework. Samples are converted into rasterized time-domain images, providing a computationally efficient input representation suitable for embedded systems. Several custom model architectures were designed and benchmarked in terms of accuracy, model size, and inference performance using a dataset containing approximately 40,000 labeled images. In addition to offline testing, the models were integrated into a GNU Radio signal processing chain for real-time evaluation. The results show that compact models can achieve high detection accuracy while maintaining low computational requirements, making them suitable for embedded radio-frequency monitoring applications. Compared with existing spectrogram-based RF detection methods, the proposed approach eliminates frequency-domain preprocessing and achieves comparable accuracy with significantly reduced computational cost.
Innovation
Methods, ideas, or system contributions that make the work stand out.
lightweight CNN
time-domain image
software-defined radio
real-time drone detection
embedded RF monitoring
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