Systems Engineering and Electronics ›› 2023, Vol. 45 ›› Issue (10): 3122-3131.doi: 10.12305/j.issn.1001-506X.2023.10.16

• Systems Engineering • Previous Articles    

Motion feature extraction and ensembled classification method based on radar tracks for drones

Jia LIU1, Qunyu XU2,*, Weishi CHEN3   

  1. 1. Research Institute for Frontier Science, Beihang University, Beijing 100191, China
    2. 2 Research Institute of Civil Aviation Law, China Academy of Civil Aviation Science and Technology, Beijing 100028, China
    3. Airport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing 100028, China
  • Received:2021-09-18 Online:2023-09-25 Published:2023-10-11
  • Contact: Qunyu XU

Abstract:

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.

Key words: drone detection, radar target recognition, feature extraction, supervised learning

CLC Number: 

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