低空目标多频段雷达识别特性数据集OA

Analysis and comprehensive identification of radar characteristics of low-altitude targets,such as unmanned aerial vehicles(UAVs),play a vital role in various fields,including low-altitude economy applications,air traffic man-agement,urban counter-drone operations,and airport safety surveillance.Technologies such as deep learning and large-scale models have been widely applied in radar target recognition research.However,existing datasets are primarily gen-erated in laboratory simulation environments.The few available real-world measurement datasets provide limited radar frequency coverage and environmental diversity,thereby failing to adequately meet practical research needs.To address this limitation,this study releases a publicly available dataset of radar target characteristics in the S-,X-,and Ku-bands,focusing on small and lightweight UAVs,birds,and meteorological clutter.This multi-frequency,multi-scenario,and multi-type dataset includes wideband and narrowband radar data for six categories of small and light-weight UAV targets,two types of bird targets,and four typical meteorological conditions.It serves as a benchmark for tasks such as radar echo-level genuine/fake classification,fine-grained classification,multimodal fusion recognition of wideband and narrowband signals,algorithm design and validation,intelligent model transferability assessment,and in-trinsic feature extraction and comparison of low-altitude targets.The dataset provides a standardized foundation for radar-based low-altitude target recognition.Comparative analyses of typical scenarios,including"same target,different radar bands"and"different targets,same radar band"were conducted to investigate the wideband and narrowband echo char-acteristics of targets.Using the proposed dataset,a convolutional neural network-based genuine/fake classification algo-rithm for UAV targets was validated.Representative examples of classifying and identifying small and lightweight UAVs and other targets,including birds and meteorological clutter,are also presented.The dataset provides high-quality sup-port for the development of intelligent radar recognition algorithms.