This research focuses on an improved automatic target recognition algorithm for solving the classification challenge of ground-moving targets from pulsed-Doppler radar. First, it was studied how decision-making intervals affect the proposed algorithm. Second, the altering of the data augmentation process was investigated. Third, a consideration of the three time-frequency signal representations and finally the use of different deep learning models for the classification issues were examined. It is proven that the proposed algorithm can efficiently recognize all targets enclosed in the publicly available RadEch dataset, with 4 s of radar echoes. When the decision-making time is only 1 s, a classification probability of 99.9% was obtained, which is an improvement related to the other research studies in this area. Furthermore, when the decision-making time is reduced 16 times the classification accuracy is reduced by only 1.3%. Moreover, the proposed algorithm was successful on another dataset enclosing ground-moving targets from comparable pulsed-Doppler radar.
The usage of Unmanned Aerial Vehicles (UAVs) is accessible for different applications to a wide range of users. However, this wide range of users raises a great concern about the threat (passive or active threats) of malicious actors who can use UAVs for criminal activities. The detection of UAVs is considered to be the first step in the process of UAVs countering (c-UAV). Nowadays, the c-UAV applications offer systems that include different sensors such as electro-optical, thermal, acoustic, radar and radio frequency sensors. Information gathered by these sensors can be fused in order to increase the reliability of threat's detection, classification and identification. It is necessary to have datasets from the different sensors in order to develop methods and algorithms for detection and classification of UAVs. This paper presents a dataset of communication signals between the drone and the control station that is used in the process of detection and classification.
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