Comparison of spectrogram, persistence spectrum and percentile spectrum based image representation performances in drone detection and classification using novel HMFFNet: Hybrid Model with Feature Fusion Network

Abstract

Drone detection and classification are gaining great importance in military and civilian applications because of their availability and widespread usages. This study presents a new framework, named as Hybrid Model with Feature Fusion Network (HMFFNet), for drone detection and classification using Radio Frequency (RF) signals. It is composed of three stages. In the first stage, 1D RF signals are segmented to specific sizes and each segment is transformed into 2D signals in terms of spectrogram, persistence spectrum and percentile spectrum images to be employed in the well-known deep learning architectures through transfer learning approach. In the second stage, VGG19, one of these architectures, is utilized for feature extraction using the transformed images. Then the features are fused in various combinations to improve their discriminative property. Finally, these features are processed by a Support Vector Machine (SVM) classifier in the last stage of the proposed framework. To evaluate the performance of the HMFFNet, numerous experiments were conducted on the publicly available DroneRF dataset. The experimental results showed that the performance of the novel percentile spectrum based features are better compared to the widely-used and good-performing spectrogram based features in drone detection and classification. Also, fusion of features from three drone representing images further improve the performance of the proposed frameworks. For the quantitative evaluation, experiments yield to the accuracy rates of 100%, 99.55%, and 97.75% for 2-Class drone existence, 4-Class drone type detection and 10-Class drone mode detection, respectively, showing higher results than that of other state-of-the-art studies.