Unsupervised radar signal sorting method based on deep clustering

Abstract

The invention relates to an unsupervised radar signal sorting method based on deep clustering. The method comprises the following steps: carrying out the signal modulation format sorting of a to-be-sorted time sequence signal through an unsupervised sorting model, and obtaining a radar signal sorting result. The unsupervised sorting model is obtained through the combination of a deep self-coding network layer and a time sequence signal clustering layer, the deep self-encoding network layer comprises an encoding layer and a decoding layer, the encoding layer compresses an input time sequence signal into a more compact potential feature representation vector in a dimensionality reduction manner, and the time sequence signal clustering layer performs unsupervised clustering analysis on the potential feature representation vector to obtain the radar signal sorting result; and a reconstruction loss function and a KL contrast divergence loss function are used as a total cost function of the model, network weight parameters and a clustering center are reversely updated by minimizing the total cost function, and joint optimization training is performed on the unsupervised sorting model. The whole training process of the model is unsupervised, and efficient and accurate sorting of radar signals can be realized.

Description

technical field [0001] The invention belongs to the technical field of electronic countermeasures and artificial intelligence, and in particular relates to an unsupervised radar signal sorting method based on deep clustering. Background technique [0002] In the military field, the research on the identification of radar communication signal modulation format is of great significance. By identifying the modulation format of enemy radar signal to restore the enemy's radar communication signal, and then further perform demodulation analysis, protocol identification, information analysis and other operations, only sufficient Only by mastering the communication information of the enemy can we gain the initiative in electronic warfare. Therefore, radar communication signal modulation format recognition has become a key technology in military fields such as electronic warfare. In the field of deep learning, supervised radar communication modulation format identification has achie...

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Problems solved by technology

Therefore, the unsupervised recognition technology of radar communication signal modulation format is difficult to achieve through traditional clustering methods

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