Deep learning is easily attacked by adversarial examples. Taking communication modulation recognition as an example, adding adversarial perturbations to the transmitted signal can effectively prevent non-cooperative users from utilizing the deep learning method to recognize the modulation of the signal. Thus, adversarial perturbations can help enhance communication security. To address the problem that the existing adversarial attack techniques are difficult to meet the adaptive and real-time requirements, the universal adversarial perturbation applicable to the whole dataset was obtained by the principal component analysis of the adversarial perturbation generated by a small part of the data extracted from the dataset. The computation of the universal adversarial perturbation can be carried out under offline conditions and then added to the signal to be transmitted in real time, which can satisfy the real-time requirements of communication and realize the purpose of reducing the accuracy of non-cooperative party modulation recognition. Experimental results show that the proposed method has better deception performance relative to the baseline method.

To address the problem of mitigating asynchronous non-stationary interference in single-channel conditions, a data-driven sparse component analysis method was proposed. The aim of this method is to recover the desired signal from the received mixed signals. This method used the powerful modeling ability of deep convolutional neural network to model the complex mapping between the input and output data, and realized the adaptive selection of sparse domain of target signals, the adaptive learning of sparse representation of target signals in sparse domain, and the automatic recovery of target signals. Unlike the previous interference mitigation algorithms, the proposed method completed the “end-to-end” signal waveform recovery in the time domain, and had no prior requirement for aliasing observation, which was more universal than the existing methods. Simulation experiments verified the effectiveness of the proposed interference mitigation method under different environmental noise and interference signal strength and generalization test conditions, and the interference mitigation performance is significantly better than the existing algorithms.

Cognitive electronic warfare is usually defined as a form of electronic warfare that is based on electronic warfare equipment with cognitive performance and focuses on autonomous interactive electromagnetic environment learning capability and dynamic intelligent confrontation task processing capability. Since it was first proposed, it has attracted extensive attention from researchers and scholars at home and abroad for its advantages of accurate perception, strong reasoning and fast decision-making. With the continuous emergence of new concepts, technologies and applications of artificial intelligence, cognitive electronic warfare has stepped into a brand new stage of development. In order to capture its future development direction, the connotation of the concept of cognitive electronic warfare was summarized and enriched from the perspective of artificial intelligence, the development of cognitive electronic warfare and typical foreign projects were sorted out, the framework and architecture of cognitive electronic warfare system was built, a comprehensive and systematic review of the key technologies of cognitive electronic warfare was conducted from the aspects of perception, judgment, decision-making, etc., and the challenges and development trends of cognitive electronic warfare were summarized.