In order to solve the problems of high dimension of reconstructed feature vector, low computational efficiency and poor robustness of existing phase space based individual recognition methods, SEI (specific emitter identification) framework based on reconstructed attractors was proposed from the perspective of nonlinear dynamics. Within the proposed framework, a novel SEI technology based on Isomap (isometric mapping) was developed. The technology used Isomap to reconstruct the emitter attractor from phase space, which can describe the dynamic characteristics of the emitter system in a lower dimension and reflect the “fingerprint” characteristics of the emitter individual. Experiments show that the proposed method can achieve higher accuracy, higher efficiency and better robustness.

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.

With the development of wireless communication technology, the electromagnetic environment has become more and more complex. Conventional signal identification methods are difficult to accurately identify illegal devices. However, electromagnetic signals have an unavoidable device-specific characteristic unintentionally generated by a transmitter, appearing in the form of an UnIntentional Modulation (UIM), namely Radio Frequency Fingerprint (RFF). RFFs can be used to uniquely identify an emitter to match a received signal with its source. In this paper, the authors propose a novel RFF scheme to separate UIM part from the original signals from the time and frequency domain, and then utilize non-Gaussian measuring tools to extract a set of dimension-reduced secondary features. Additionally, Singular Value Reconstruction (SVR) is developed to extract UIM in the frequency spectrum. In time domain, a curve-fitting residual method is proposed to extract the UIM on the estimated instantaneous phase based on Maximum Likelihood Estimator (MLE). Various aspects of the proposed method are evaluated, including identification accuracy under various Signal-to-Noise Ratio (SNR) conditions, energy relationships between the UIM and the whole signal, and sensitivity to training set size. Compared with other methods, experimental results based on real-world signals prove that the proposed method has remarkable performance and high practicability.