基于宽窄带综合的飞机机型识别方法OA

This paper proposes an aircraft type recognition method based on the fusion of wideband and nar-rowband data to improve the limited fine-grained recognition capability for aircraft targets of traditional sin-gle-modal radar.Firstly,convolutional network is used to mine the local scattering characteristics of wideband one-dimensional range profiles,and then recurrent neural network employed to capture the contextual features of narrowband micro-Doppler signatures at various time points,so as to achieve the purpose of complementary infor-mation.At the same time,an adaptive decision fusion network is designed to handle the imbalance between the two modalities during decision fusion,thus achieving aircraft type recognition.Finally,the simulation data of sev-en types of aircraft targets in wide and narrow bands are used for experimental verification.The simulation results show that the average recognition accuracy of the proposed method is 76.7%,which is over 6.4%higher than that of the traditional methods.