Ground-Based Radar Detection Dataset of “ Low Slow Small” Unmanned Aerial Vehicles Under Simple Field Background Conditions

‍ ‍Owing to its inherent low radar cross section and complex flight patterns， it is severely challenging for a radar system to detect， track， and identify unmanned aerial vehicles （UAVs）， which has become a hotspot and difficult point in the field of low altitude safety research. However， the currently publicly released radar datasets are still extremely scarce， and the quantity of radar echo datasets of “low slow small” target is even more scarce， whereas the target types， motion states， and experimental scenarios are single. In response to the above issues， this article provides a dataset of ground radar detection of “low slow small” UAVs under simple field background conditions through carefully designed experimental scenarios， field data acquisition， and data processing. This dataset covers five models in two types of UAVs targets （i.e.， multi-rotor and fixed wing）. The trajectories include from far to near， from near to far， and entering and exiting radar beams. The target motion states include acceleration， deceleration， hovering， climbing， diving， and heading adjustment. The data signal-to-noise ratios （SNRs） cover high and low SNR. It fully considers the urgent need for measured data in the research of radar signal processing methods for “low slow small” target characteristics analyzing， detection， tracking， and recognition； additionally， it provides a comprehensive and diversified data resource for relevant scholars. It can effectively promote the development of radar detection and recognition technology of “low slow small” target.