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This paper presents a novel method for radar emitter recognition that combines parameter set clustering with decision tree classification to improve accuracy in noisy environments. The proposed method utilizes Mean Shift clustering to extract frequent item sets from radar pulse signals, which are then used to train a decision tree classifier for identifying unknown radar signals. Simulation results demonstrate that this approach outperforms traditional methods like LSTM and SVM, particularly in scenarios wi

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winnertime4me

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EN