AMC (automatic modulation classification) plays a vital role in spectrum monitoring and electromagnetic abnormal signal detection. Up to now, few studies have focused on the complementarity between features of different modalities and the importance of the feature fusion mechanism in the AMC method. This paper proposes a dual-modal feature fusion convolutional neural network (DMFF-CNN) for AMC to use the complementarity between different modal features fully. DMFF-CNN uses the gram angular field (GAF) image coding and intelligence quotient (IQ) data combined with CNN. Firstly, the original signal is converted into images by GAF, and the GAF images are used as the input of ResNet50. Secondly, it is converted into IQ data and as the complex value network (CV-CNN) input to extract features. Furthermore, a dual-modal feature fusion mechanism (DMFF) is proposed to fuse the dual-modal features extracted by GAF-ResNet50 and CV-CNN. The fusion feature is used as the input of DMFF-CNN for model training to achieve AMC of multi-type signals. In the evaluation stage, the advantages of the DMFF mechanism proposed in this paper and the accuracy improvement compared with other feature fusion algorithms are discussed. The experiment shows that our method performs better than others, including some state-of-the-art methods, and has superior robustness at a low signal-to-noise ratio (SNR), and the average classification accuracy of the dataset signals reaches 92.1%. The DMFF-CNN proposed in this paper provides a new path for the AMC field.

Automatic modulation classification (AMC) is vital in cognitive communication systems. Existing AMC methods are mainly designed for Gaussian noise channels, but research shows that non-Gaussian impulsive noise in wireless communication systems cannot be ignored. This paper proposes a novel AMC method based on Log-domain 3D constellation diagrams and multi-scale dual-convolutional 3DCNN (3D Convolutional Neural Networks). Firstly, we adopt non-linear logarithmic function transformation to effectively suppress non-Gaussian impulsive noise and transform multi-type modulated signals from the signal time domain to the graph domain, constructing Log-domain constellations. Secondly, the 2D constellations are projected onto three-dimensional space to form logarithmic domain 3D constellation diagrams, aiming to provide more discriminative feature dimensions for deep learning networks. Then, a new multi-scale dual-convolutional 3DCNN (MDC-3DCNN) is proposed, where the number and scale of dual-convolutional structures are the hyperparameters of MDC-3DCNN, to achieve feature extractions on both spatial and planar dimensions simultaneously for modulation classification. Numerical simulation results and actual measurement experiments demonstrate that the proposed method can effectively perform high-precision and robust classification of modulated signals in non-Gaussian impulsive noise channels.

Array synthetic aperture radar (SAR) three-dimensional (3D) imaging can obtain 3D information of the target region, which is widely used in environmental monitoring and scattering information measurement. In recent years, with the development of compressed sensing (CS) theory, sparse signal processing is used in array SAR 3D imaging. Compared with matched filter (MF), sparse SAR imaging can effectively improve image quality. However, sparse imaging based on handcrafted regularization functions suffers from target information loss in few observed SAR data. Therefore, in this article, a general 3D sparse imaging framework based on Regulation by Denoising (RED) and proximal gradient descent type method for array SAR is presented. Firstly, we construct explicit prior terms via state-of-the-art denoising operators instead of regularization functions, which can improve the accuracy of sparse reconstruction and preserve the structure information of the target. Then, different proximal gradient descent type methods are presented, including a generalized alternating projection (GAP) and an alternating direction method of multiplier (ADMM), which is suitable for high-dimensional data processing. Additionally, the proposed method has robust convergence, which can achieve sparse reconstruction of 3D SAR in few observed SAR data. Extensive simulations and real data experiments are conducted to analyze the performance of the proposed method. The experimental results show that the proposed method has superior sparse reconstruction performance.

Linear array synthetic aperture radar (SAR) can achieve three-dimensional (3D) high-resolution imaging, which provides a novel scattering diagnosis technique. However, high sidelobes and background noise are the main challenges of scattering diagnosis based on SAR image. Sparse imaging can improve image quality, such as suppressing sidelobes and noise. $L_{1}$ regularization is an efficient and typical model for sparse imaging. However, the $L_{1}$ regularization, as a convex optimization method, often introduces bias in amplitude estimation, which has a negative impact on scattering diagnosis. Therefore, in this paper, 3D imaging method based on complex-valued minimax concave penalty (CMCP) and improved alternating direction method of multipliers (IADMM) is presented to obtain high-quality and high-accuracy 3D SAR images for scattering diagnosis. Compared with the existing sparse imaging method based on $L_{1}$ regularization, the proposed method not only improves the image quality, but also reduces the bias effect, which can be applied for scattering imaging. Additionally, IADMM significantly reduces computational complexity. The experimental results indicate that the proposed method has compelling reconstruction accuracy of SAR scattering image.

Direction of arrival (DOA) estimation is an effective method for detecting various active interference signals during the satellite navigation process. It can be utilized for both interference detection and anti-interference applications. This paper proposes a DOA estimation algorithm for satellite interference sources based on frequency domain covariance matrix reconstruction (FDCMR) to address various types of active interference that may occur in the satellite navigation positioning process. This algorithm can estimate the DOA of coherent signals from multiple frequency points under low signal-to-noise ratio (SNR) conditions. The signals received from the array are transformed from the time domain to the frequency domain using a fast Fourier transform (FFT). The data corresponding to the frequency point of the target signal is extracted from the signal in the frequency domain. The frequency domain covariance matrix of the received array signals is reconstructed by utilizing its covariance matrix property. The spatial spectrum search method is used for the final DOA estimation. Simulation experiments have shown that the proposed algorithm performs well in the DOA estimation under low SNR conditions and also resolves coherency. Moreover, the algorithm’s effectiveness is verified through comparison with three other algorithms. Finally, the algorithm’s applicability is validated through simulations of various interference scenarios.

With the development and maturity of compressed sensing theories, sparse signal processing has been widely applied to synthetic aperture radar (SAR) imaging. L ₁ regularization, as an effective sparse reconstruction model, can enhance sparsity of the scene. However, due to the convexity of the L ₁ regularization function, the L ₁ -based sparse reconstruction frequently introduces bias estimation, resulting in an underestimation of target amplitudes. Furthermore, the 3-D SAR imaging scene has diverse features, which are difficult to characterize solely with the L ₁ regularization. Therefore, in this paper, we propose a novel imaging framework for 3-D array SAR imaging, which combines hybrid regularization function and an improved variable splitting with the method of multipliers (IVSMM). Firstly, we present the hybrid regularization function, which combines smoothly clipped absolute deviation (SCAD) and high-dimensional total variation (HTV) to reduce bias effects and preserve the regional features of the target. Then, we present IVSMM to solve the optimization problem of the hybrid regularization, effectively reducing the computational complexity required for 3-D imaging. Finally, the excellent reconstruction performance of the proposed method is validated through a series of simulations and experimental data.