Radar radiation source recognition method based on deep learning strategy and multitask learning strategy

Abstract

The invention discloses a radar radiation source recognition method based on a deep learning strategy and a multitask learning strategy and mainly aims to solve the problem that recognition accuracy is low in the prior art. The method comprises the implementation steps that 1, an original radar radiation source signal is subjected to data preprocessing; 2, envelope features, fuzzy function features, slice features, cyclic spectrum features and frequency spectrum features of the preprocessed radar radiation source signal are extracted, and values of the features are linearly transformed into [0,255] and saved as an image set; 3, a convolutional neural network (CNN) is designed, and the multitask learning strategy and a random inactivation strategy are introduced into the CNN; and 4, four feature training sets are used to train the CNN, then four trained CNN models are utilized to classify four feature test sets, and a radar radiation source recognition result is output. The method is high in recognition accuracy and can be applied to electronic intelligence reconnaissance, electronic support reconnaissance and radar threat warning systems.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a radar radiation source identification method, which can be used in electronic intelligence reconnaissance, electronic support reconnaissance and radar threat warning systems. Background technique [0002] Radar emitter signals are extracted through features, and their individual characteristics are represented by convolutional neural networks. In order to prevent network overfitting, multi-task learning and random deactivation strategies are introduced to uniquely and accurately identify individual emitters. [0003] Radar radiation source identification is a key processing process in electronic intelligence reconnaissance ELINT, electronic support reconnaissance ESM and radar threat warning RWR system, and it is also the premise and basis of electronic jamming. With the rapid development of radar technology and the continuous improvement of modern electronic counterm...

AI Technical Summary

Problems solved by technology

However, this method is less robust

[0014] The above methods cannot take into account the various forms of radiation source signals, variable frequency, and time-frequency overlap. The generalization performance of the model is poor. How to further extract more subtle robust features and improve the generalization performance of the system has become a radar radiation source. key to identification

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