无人机雷达航迹运动特征提取及组合分类方法

The radar echoes of birds and drones target have high similarity, which make it difficult to distinguish them. Therefore, the spatio-temporal characteristics of target tracks formed by drones, birds and dynamic precipitation clutter are studied, and the differences in motion mechanisms and behavior patterns between drones and birds are analyzed. A motion feature extraction method based on target tracks is proposed and target feature vectors are constructed. Based on the measured track data of the target provided by the detection bird radar system, a training and test sample set is established. The supervised learning method combined with the random forest model is used to distinguish the target tracks of drones, birds and precipitation clutter. The experimental results show that the correct recognition rate of drone targets over a wide area can reach over 85%, and the classifier model has high calculation efficiency, strong sample adaptability, and good universality and practical value.