Abstract: A specific emitter identification (SEI) method based on deep reinforcement learning is proposed on account of the deficiency of emitter individual feature extraction depending on artificial experience. Due to the differences of the transient information of signal envelope, which results from the change of the signal owing to a nonideal transmitter channel, an envelope rising edge is used as the input state of deep neural network, and the emitter classifications are used as the optional actions of the current input state. The envelope features are extracted automatically through the convolutional neural network (CNN), and Q values of the current state action pairs are fitted, thus completing the specific emitter identification task based on the reinforcement learning model. The applications of deep Q network (DQN), deep double Q network (DDQN) and Dueling network in the specific emitter identification are discussed. The measured results show that the recognition rate of traditional machine learning algorithm is less than 80%, but the deep reinforcement learning model can achieve the high recognition rate of 98.42%. Key

Key words: radar, specificemitteridentification, deepneuralnetwork, reinforcementlearning