Experimental Study of Trajectory Features for the Recognition of Low-Flying Low-Speed Radar Targets Using Passive Coherent Radar Systems

Abstract

Introduction. Small unmanned aerial vehicles (UAVs) are a growing threat due to their possible use for illegal activities. Currently, passive coherent radar systems are widely used to detect, track and recognize moving targets, including small UAVs, which makes them a promising tool for use in modern airspace radar monitoring systems. At the same time, recognition of small UAVs becomes a challenging task due the possibility of confusing them with birds, particularly in maritime areas with large bird populations. In a search for new solutions to the problem of recognizing small UAVs, trajectory features can be used.Aim. To analyze differences between the trajectory features of low-flying low-speed targets in order to verify the possibility of their use for recognition purposes.Materials and methods. Real radar measurements of UAVs and birds obtained by a passive coherent radar system were used. Specific characteristics of the trajectory parameters of target classes were built using computer statistical modeling in the MatLab environment. Differences in the movement trajectory of targets were established by comparative analysis.Results. Significant differences between the flight path of UAVs and birds were found. Specific features of the trajectory of small aerial targets of each type were investigated. On the basis of radar measurement, graphs of the characteristic trajectory parameters of UAVs and birds were plotted. The conducted comparative analysis allowed identification of the characteristics of the flight path of each target type in each movement segment. Trajectory features that can be used for recognition purposes were identified.Conclusion. The practical significance of the proposed trajectory features and the possibility of their implementation in the development of an algorithm for recognizing low-flying low-speed radar targets using passive coherent radar systems was established. The knowledge of differences between the flight path of UAVs and birds can improve the quality of the UAV recognition problem.