A Polarization Identification Method of Full Polarization Phased Array Radar and Active Decoy

Aiming at the problem of too few identification methods for phased array radar and active decoys and the low recognition rate, this paper proposes a polarization identification algorithm combining polarization descriptor and Multivariate Long Short Term Memory Fully Convolutional Network (MLSTM-FCN) neural network. Firstly, this paper realizes the accurate modeling of fully polarized phased array radar and active decoys in detail, and analyzes the polarization characteristics in spatial domain. The polarization characteristics of full polarization phased array radar and active decoy are distributed differently in the same spatial domain. The sample datasets of polarization descriptors of polarized phased array radar and active decoys in spatial domain is obtained. Finally, we train the recognition model with MLSTM-FCN neural network. The simulation results show that the recognition rate is above 92% at a low signal-to-noise ratio (SNR) of 7 dB.