A Radar Compound Jamming Recognition Method Based on Blind Source Separation

In modern times, radar detection faces the additively compounded jamming signals emitted by multiple jammers. Due to the unknown number, type, and parameters of individual jamming signals, previous jamming recognition algorithms cannot identify all compound cases. During network model training, it becomes necessary to predefine the type of compound jamming signals, and it also limits the number of labels for samples, from which only specific compound cases can be recognized. In this article, a recognition strategy named "Separation + Recovery + Recognition" is proposed to identify all the jamming cases of additively compounded jamming effectively. First, the number of signal sources of the received signals from multiple channels is analyzed. Next, the separated single jamming signal is obtained by source separation method from the received additively compounded signals. And then the radar signal recovery network is used to compensate and recover signals that are incompletely separated due to the time-frequency overlap. Finally, the separated and recovered single jamming signals are put into the designed convolutional neural network model for recognition. The simulation results show that the proposed algorithm demonstrates superior performances in recognition and generalization. When the jamming-to-noise ratio is −10 dB, the recognition accuracy of the compound case of five jamming signals can still reach 90% plus.