Optimizing RF-Sensing for Drone Detection: The Synergy of Ensemble Learning and Sensor Fusion
Laiba Tanveer, Muhammad Zeshan Alam, Maham Misbah, Farooq Alam Orakzai, Ahmed Hussein Alkhayyat, Zeeshan Kaleem · 2024
Unmanned Aerial Vehicles (UAVs) find extensive applications across various industries, surveillance, and communication services. However, concerns regarding their potential misuse have prompted the development of counter-drone measures. In this paper, we propose a counter-UAV approach centered on radio frequency (RF) signal sensing. Upon the detection of an RF signal, our system employs a Short-Time Fourier Transform (STFT)-based spectrogram (SP) generation process. This SP is further refined through adaptive windowing and logarithmic tuning to extract multi-intensity features. To classify the complex RF time-domain signals and STFT spectrograms, we utilize two deep learning classifiers: RF-Network and SP-Network, facilitating a multi-class classification process by using deep neural networks (DNN). To enhance the overall accuracy of our model, we leverage an ensemble neural network (EN-Net) by combining predictions from the RF-Network and SP-Network classifiers. Fusing data from a single sensor in both time and frequency domains enhances DNN accuracy by providing complementary information, improving robustness, and reducing overfitting, resulting in increased model performance and a deep understanding of the data. Our results demonstrate a notable improvement in accuracy—specifically, a 36% increase for multi-class models when compared to single-class models. This proves the effectiveness of our EN-Net model in addressing security threats posed by UAVs through advanced RF signal analysis and classification.