Radar Emitter Individual Identification Based on Parameter Optimization VMD and LightGBM

In order to solve the problem of low accuracy of radar emitter individual identification in complex electromagnetic environment, a radar emitter individual identification technology based on parameter optimization VMD and LightGBM is proposed. Firstly, the unintentional features of the radar emitter are analyzed, and the added phase noise is taken as the fingerprint feature of radar emitter in the simulation. Secondly, sparrow search algorithm (SSA) is used to automatically optimize the decomposition parameters of variational modal decomposition (VMD), and the optimal decomposition parameter combination is accurately and quickly obtained as [2, 2 950]. Then, based on the optimal VMD decomposition parameters, the energy entropy and sample entropy of the emitter signal are extracted as feature vector. Finally, the feature vector is sent to the LightGBM classifier to complete the emitter individual identification. Through the verification of measured data, the recognition rate can reach more than 85% when the signal-to-noise ratio is 25 dB, which has ideal recognition results.