Internet of Things (IOT) technology provides a large-scale network for information exchange and communication with big data. Because of the openness of IOT devices in the process of signal transmission, the recognition and access of different IoT devices are directly related to the wide application of its system. The Radio Frequency Fingerprinting (RFF) is a unique characteristic closely related to the hardware of IoT devices themselves, which is difficultly tampered. In this paper, four kinds of RF fingerprint feature extraction algorithms based on statistical features are studied. Robust Principle Component Analysis (RPCA) is used for the dimensionality reduction and the Support Vector Machines (SVM) is used for classification. Through theoretical modeling and experimental verification, the reliability and distinguishability of RFFs are extracted and evaluated, and the classification results are displayed in the real Internet of Things equipment environment.

As electromagnetic environments are increasingly complex, there are more kinds of radar jamming signals. Active jamming recognition has problems of the low recognition accuracy and the high computational complexity, especially under a low jamming‐to‐noise ratio (JNR). Herein, a deep learning network based on bilinear EfficientNet and attention mechanism is proposed to recognise and classify eight kinds of jamming signals automatically. Firstly, the one‐dimensional interference signal is transformed into a two‐dimensional time–frequency image by the time–frequency analysis. Based on the transfer learning of EfficientNet‐B3, the effective features of the time–frequency image are automatically extracted by the two‐way network with attention mechanism, and the active interference classification is realised. The experimental results show that the method's overall recognition rate for eight kinds of interference signals is more than 97.5% when the JNR is −8 dB and is close to 100% at −2 dB.

Aiming at the existing deep learning radar signal modulation recognition method are mostly based on timefrequency image (TFI), resulting in a large number of network model parameters due to image contains a large amount of redundant information, this paper proposes a radar signal intrapulse modulation recognition method based on point cloud which removes redundant information. Radar signals of different modulation types are mapped into point cloud after Smoothed Pseudo Wigner-Ville Distribution (SPWVD) transformation. Then, PointNet++ is used to classify the point cloud data according to its modulation type and output its corresponding modulation type labels. Simulation results show that the proposed method can effectively recognize radar signals of typical modulation types, and show strong effectiveness and reliability at low signal-to-noise ratio (SNR). Besides, the use of a lightweight model makes the operation of the method more efficient.

Existing methods for generating synthetic aperture radar (SAR) deception jamming signals have slow speed, low imaging quality, and insufficient intelligence in complex electromagnetic environments. This paper proposes a deep learning‐based SAR deception jamming signal generation method based on deep echo inversion Unet (DEIUnet). This method has high speed and provides high‐image quality of the interference signal. A Swin Next (SN) block is proposed to combine local and non‐local information in the image and echo data. The Unet structure consists of SN blocks, and a residual connection is used as the jump connection to fuse the multi‐scale feature information from the echo and image data. PixelShuffle is utilised for up‐sampling to generate high‐quality echo data. The experimental results on MSTAR and Sentinel‐1 data sets verify the effectiveness and superiority of DEIUnet for echo inversion. The imaging results of the SAR deception jamming signal generated by DEIUnet on an MSTAR scene confirm the effectiveness of the proposed method.