Jiansheng Bai

UAV radio-frequency (RF) signal identification is an important technique for low-altitude security monitoring. However, background noise interference and distortions in amplitude and phase features can reduce the robustness of methods based on a single frequency-domain representation. A one-dimensional power spectrum alone provides limited capability for characterizing pairwise relationships among frequency components, whereas two-dimensional feature representations may weaken spectral-line continuity. To address these limitations, this paper proposes a Power Spectral Density-Frequency-domain Gramian Angular Field Dual-stream Fusion Network (PFG-DSFNet). First, each RF observation window is converted into a power spectral density (PSD) sequence and further mapped to a frequency-domain Gramian Angular Field (F-GAF) image. Then, the PSD sequence and F-GAF image are used to extract the spectral envelope and heterogeneous two-dimensional frequency-domain features, respectively. Furthermore, an Adaptive Gated Low-rank Bilinear Interaction (AGLRBI) module is designed for the dual-stream features to model second-order interactions and perform adaptive fusion. In this way, the dual-stream representation framework enhances high-accuracy UAV RF signal identification. Experiments on the DroneRF 10-class classification task show that PFG-DSFNet achieves an accuracy of 99.35% and a Macro-F1 score of 0.9934. Ablation experiments further show that the bilinear interaction and adaptive gating components each contribute to the final performance, and their combination achieves the best result. Additional experiments under mixed-SNR conditions and in measured scenarios further support the robustness of the proposed method.

Particle-laden pipe flows are ubiquitous in food, chemical and pharmaceutical processes, where solid particles significantly alter fluid deformation and mixing. Understanding these transport mechanisms is critical for process optimisation. A Lagrangian analysis framework based on a SPH-DEM simulation is proposed to compute finite-time Lyapunov exponent (FTLE) fields and extract Lagrangian coherent structures (LCSs) for non-Newtonian particle-laden pipe flows. The method directly exploits the inherently Lagrangian particle trajectories and computes the FTLE fields using the SPH interpolation scheme, avoiding the costly numerical integration required by conventional Eulerian approaches. Subsequently, LCSs are extracted via a ridge detection algorithm and the combined FTLE is introduced to quantify mixing intensity. The framework is validated against the Kármán vortex street benchmark, showing good agreement with experiment and numerical results. Then the validated framework is applied to non-Newtonian particle-laden pipe flows for a wide range (0 vol.%~30 vol.%) of particle loading. Results reveal a critical concentration range of 20 vol.%~30 vol.%, where the cross-sectionally average combined FTLE increases with concentration up to 20 vol.%, indicating enhanced mixing, but decreases beyond 30 vol.% as particle–particle interactions suppress near-wall fluid deformation. These findings provide a robust Lagrangian tool and new quantitative insights for optimising mixing and transport in industrial particulate flows, such as in food processing pipelines and chemical reactors.

To address the issues of sample imbalance, unstable generation quality, and insufficient feature extraction in spectrum anomaly signal detection under complex electromagnetic environments, this paper proposes a VAE-GANGP identification model that integrates a Variational Autoencoder (VAE) with a Gradient Penalty-based Generative Adversarial Network (GAN-GP). First, the VAE is employed to encode the original spectrum, generating structured latent features that follow a standard normal distribution. This replaces the random noise input in traditional GANs, significantly enhancing the semantic consistency of generated samples and training stability. Second, an adversarial training mechanism based on Wasserstein distance with gradient penalty (WGAN-GP) is introduced, effectively mitigating mode collapse and gradient vanishing, thereby improving the model’s capability to fit complex signal distributions. Furthermore, a multi-objective optimization function combining reconstruction error and adversarial loss is constructed, establishing an end-to-end integrated framework for feature learning, signal reconstruction, and anomaly discrimination. Experiments are conducted using a synthetic dataset comprising various modulation types and simulated environments with different signal-to-noise ratios for systematic validation. The results demonstrate that the spectrum data generated by VAE-GANGP closely matches the distribution of real signals. Under AWGN-dominated synthetic test conditions, the model achieves an anomaly detection accuracy of 98.1%. When evaluated under more realistic channel impairments (phase noise, multipath, impulsive interference), the model maintains competitive performance, outperforming existing methods and demonstrating promising potential for practical electromagnetic spectrum monitoring. Its performance significantly surpasses traditional detection methods and single deep learning models, providing a highly reliable and adaptive solution for spatial electromagnetic spectrum anomaly detection.

Reconfigurable Intelligent Surfaces (RIS), as the core technology for 6G wireless communications and Integrated Sensing and Communication (ISAC), play a vital role in enhancing signal quality and improving communication efficiency. To address the critical issue of damaged or contaminated RIS elements during design and deployment, this paper proposes a failure diagnosis method based on Electromagnetic Physical Constraints (EMPC). First, a low-dimensional measurement matrix is generated via spatial encoding of sparse signals from impaired RIS elements, subject to physical constraints imposed by the RF device structure. Subsequently, to accurately model the multi-modal characteristics of signal distributions in multipath channels, a Scaled Gaussian Mixture (SMG) model is adopted to fit the distribution of sparse coefficients. Leveraging the spatial structural correlation within the RIS array, a center-oriented parameter propagation acceleration strategy is designed, significantly enhancing computational efficiency. Building upon this foundation, the model parameters of the RIS are mapped onto a Riemannian manifold using Information Geometry (IG) theory. This transforms the fault diagnosis problem into one of measuring the information distance between coordinates on the manifold. A fast Kullback-Leibler Divergence (KLD)-based difference detector is designed to rapidly diagnose the locations and quantity of failed RIS elements. The experimental evaluation comprehensively validates the method's performance through extensive numerical simulations (encompassing noise impacts, device parameters, diverse fault modes, and channel models) and practical measurements in a microwave anechoic chamber. The results are analyzed from multiple dimensions to discusss the method's effectiveness, robustness, and practical applicability.

In the complex electromagnetic space, due to the serious influence of all kinds of interference and environmental noise targets, the target signal is very weak and difficult to be detected. To solve this problem, a weak signal detection method based on dual-channel interference cancellation and IGWO-VMD-SVD was proposed. The data in a single channel was synchronized to solve the problem of phase difference in the received data of two channels, and the interference signal was cancelled by subtraction of the synchronized data of two channels. It introduced good point set initialization, nonlinear control parameters and dynamic weight update strategy to strengthen the search ability of GWO algorithm. IGWO was used to optimize the number of decompositions (K) and penalty factor (α) in VMD. The simulation results show that the correlation coefficient between the signal obtained by dual-channel interference cancellation after synchronization and the target signal is 0.98. In the environment of -10dB signal to noise ratio(SNR), the SNR of the output signal of IGWO-VMD-SVD reaches 7.48dB, which is increased by 174.8%. The measured experiments show that the proposed method can effectively extract the weak signal, and the lowest cross correlation coefficient can reach 0.92. It has adaptability and effectiveness in the actual complex electromagnetic signal denoising processing.

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.

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.

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.

Modulated signal detection has been rapidly advancing in various wireless communication systems as it's a core technology of spectrum sensing. To address the non-Gaussian statistical of noise in radio channels, especially its pulse characteristics in the time/frequency domain, this paper proposes a method based on Information Geometric Difference Mapping (IGDM) to solve the signal detection problem under Alpha-stable distribution (α-stable) noise and improve performance under low Generalized Signal-to-Noise Ratio (GSNR). Scale Mixtures of Gaussians is used to approximate the probability density function (PDF) of signals and model the statistical moments of observed data. Drawing on the principles of information geometry, we map the PDF of different types of data into manifold space. Through the application of statistical moment models, the signal is projected as coordinate points within the manifold structure. We then design a dual-threshold mechanism based on the geometric mean and use Kullback-Leibler divergence (KLD) to measure the information distance between coordinates. Numerical simulations and experiments were conducted to prove the superiority of IGDM for detecting multiple modulated signals in non-Gaussian noise, the results show that IGDM has adaptability and effectiveness under extremely low GSNR.

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.