Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data

Continuous Wave (FMCW) radars represent the most Unmanned Aerial Vehicles (UAV) have successfully attractive and cost-efficient solutions [2]. While for the permeated modern society with various applications for civil verification and classification task various methods exist in and military purposes. Oil and gas, construction, metals and literature employing machine learning techniques such as mining already incorporate UAVs in their processes. SVM [3], Random Forests [4], Nearest Neighbor [5] and Furthermore, UAVs are employed for commercial purposes, Deep Neural Networks [6][7][8]. More recent DNN such as the monitoring of public places, cartography, survey approaches based on convolutional neural networks are wildlife, search and rescue (SAR), first aid and delivery of introduced in Samaras et al. [9]. The authors presented a deep goods. Big technological companies continuously challenge learning classification method based on data from an X-band the status quote by announcing breakthrough services. FMCW surveillance 2D radar that is able to reach a Moreover, progress in UAV regulation has driven classification accuracy of up to 95.0% utilizing a custom investments since 2019, to further increase the popularity and CNN based architecture. A similar approach is presented in use of UAVs in sectors that present significant potential but [10] where the authors proposed Res-Net-SP, a compressed still minimal use, such as agriculture, healthcare, architecture of ResNet-18 that is based in convolutional infrastructure, property management and insurance.