基于高分辨相控阵雷达的低空无人机群目标数据集

Swarms of UAVs, flocks of birds, and other low-altitude group targets exhibit characteristics such as collective intelligence and dense distribution, resulting in complex motion patterns. These features have made them a prominent topic in the field of radar detection. Traditional association and tracking methods for such targets often lead to problems such as track interruption and incorrect target state estimation, resulting in unstable tracking outcomes. Measurements containing spatial position information of targets serve as the foundation for addressing these challenges. Some existing studies simulate measurements that resemble real tracks of targets by analyzing their motion characteristics. However, these simulations fail to effectively account for the influence of sensor errors and environmental factors on measurements. Real data are crucial for evaluating algorithmic performance in practical scenarios. Currently, there is a lack of publicly available radar datasets to support research in areas such as swarm target track association and tracking filtering. To address this gap, we present a low-altitude UAV swarm dataset based on high-resolution phased array radar. The dataset encompasses classic formation patterns observed in practical scenarios, including linear and cross-shaped formations, as well as common motion patterns such as uniform linear and circular motion. Additionally, the impact of varying target spacing on experimental outcomes was taken into consideration. The dataset comprises three experiments on UAV swarm formations, namely tightly spaced linear formations, widely spaced linear formations flying in circular and straight paths, and cross-shaped formations flying in circular and straight paths. Echo data were collected using high-resolution phased array radar. Measurements of the group target were obtained through techniques such as stepped frequency synthesis and target detection. This dataset provides real data support for validating track association and tracking algorithms, helping refine these algorithms and contributing to advancements in group target monitoring research.