The satisfaction of various basic requirements of cognitive radar by Ultra-wideband (UWB) signals makes UWB cognitive radar attract extensive attention. The variety and large dynamic range of jamming in the UWB spectrum range make jamming identification critical and challenging. However, the traditional method has low recognition accuracy, high computational complexity, and difficulty in multi-signal recognition. In this paper, we propose a multi-scale attention network (MANet) for carrier-free UWB cognitive radar to identify target signals and nine types of jamming signals. MANet extracts different fine features by multi-scale dilation convolution. The features are stitched together in the channel dimension. The subtle features that are beneficial for recognition are then substantially enhanced using channel attention blocks. The proposed method combines the time domain and frequency domain features to improve the recognition performance by using the powerful feature extraction ability and generalization ability of MANet. Simulation results show that the overall recognition accuracy of the method is 93.1%, with less storage space, shorter FLOPs and inference time than the five recognition methods, and better and more stable recognition performance is also achieved at low jamming-to-noise ratios (JNR).

Unmanned aerial vehicles (UAVs) are used in different scenarios. The need for intelligent and accurate recognition of targets using UAV-based radars is increasingly urgent. As unique information of targets, micro-Doppler characteristics have become an important basis for target recognition. For three typical ground targets including pedestrians, wheeled vehicles and tracked vehicles, the micro-Doppler modulation of the UAV vibration on echo signals is analyzed firstly in this paper. Secondly, singular value decomposition (SVD) is adopted to suppress ground clutter and environmental noise. In order to decrease the computation and increase the contrast, all colored time-frequency diagrams of micro-Doppler signals are transferred into grayscale images. Thirdly, a multi-level target recognition method based on the transfer learning of deep convolutional neural networks (DCNNs) is proposed. The first-level recognition is designed to accurately distinguish pedestrians, wheeled vehicles and tracked vehicles, while the purpose of the second-level of recognition is to determine the three states of pedestrians including stepping, walking and jogging. At last, experiments are carried out using a designed Doppler radar to prove the effectiveness of proposed method. Results indicate that method in this paper not only avoids the complex feature extraction process, but also has higher accuracy and better robustness.

In order to achieve precise operations on specified targets from the unmanned aerial vehicles (UAVs), classifying ground targets correctly is especially important. Micro-Doppler effect which provides unique information of targets has been the basis for targets classification. Due to the effect of ground clutter, noise and complex signal modulation, enhancing micro-Doppler features of UAV-to-ground targets is necessary for accurate classification. This paper firstly establishes the models of UAV-to-ground targets including wheeled vehicles, tracked vehicles and pedestrians to analyze their micro-Doppler differences. Secondly, Principal Components Analysis (PCA) is utilized to remove the ground clutter. Compared with other algorithms, PCA can use a small amount of calculation to remove the ground clutter while retain nearby micro-Doppler signals. Then, micro-Doppler signals are sparsely represented based on Fourier basis. Orthogonal Matching Pursuit (OMP) is chosen to reconstruct micro-Doppler components and refine spectral lines after random projection. The three steps make up Compressed Sensing (CS) together. At last, non-linear transform of Doppler spectrum is conducted to further enhance the distinction of micro-Doppler spectral lines. Distinguishing micro-Doppler features are extracted from pre-processed micro-Doppler signals, which eventually contributes to the accurate targets classification. Comparison with other methods is also made to prove the robustness and anti-noise performance of proposed method.

Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.

The rapid development of flight control technology has made unmanned aerial vehicles (UAVs) widely used in high-precision strikes on the battlefield. The premise of this is to achieve accurate target recognition using UAV-based radars. Aiming at three typical ground targets, including pedestrians, wheeled vehicles, and tracked vehicles, the micro-Doppler modulation caused by the random vibration of the UAV is analyzed in this article for the first time. To improve the recognition accuracy under low signal-to-noise ratios (SNRs), Doppler signals are transformed into time–frequency images, and a deep convolutional denoising encoder (DCDE) is designed to effectively remove the noise without suppressing micro-Doppler characteristics. To avoid the complicated micro-Doppler feature extraction, deep residual learning that can reduce the burden of network training and gain higher learning efficiency compared with traditional deep convolutional neural networks (DCNNs) is adopted. Recognition results under various occasions using denoised micro-Doppler images and designed residual learning network indicate that the proposed method has higher precision and better robustness than current methods. Even when the SNR is only −16 dB, the overall recognition accuracy still exceeds 90%.

Synthetic aperture radar (SAR) may be attacked by multifarious kinds of the active-jamming, which will lead to the ineffectiveness of SAR in complex electromagnetic environment. In this paper, a novel multi-electromagnetic jamming counter-measure for the airborne SAR is proposed based on maximum signal-to-noise ratio (SNR) blind source separation. Firstly, the imaging geometry and multi-component mixed signal model of airborne SAR are established. Then, based on multi-component mixed signal matrix, a blind source separation (BSS) method based on the maximum SNR is proposed to separate the real target echo signal from the multi-electromagnetic jamming signals. After real target echo signal identification, the high resolution images of the interested target area can be achieved by the corresponding SAR imaging method. The simulated and measured data results are present to prove the feasibility and effectiveness of the proposed method.