This paper investigates the effect of axial stress on acoustoelastic guided wave propagation in arbitrary cross-section. Many researchers have investigated the effect of stress on guided wave propagation in arbitrary cross-section. However, the effect of high-order elastic constants in guided waves propagation of such structures is not as well understood. This paper deals with an acoustoelastic theory combined with semi-analytical finite element (AE-SAFE) method for modeling stressed wave propagation. The dispersive solutions are obtained in terms of phase velocities, group velocities and velocity sensitivities to stress variations in complex arbitrary cross-section. These results can be utilized to select the appropriate points and are significant for stress measure in arbitrary cross-section at high frequency region. The AE-SAFE method is applied to three examples, including plates, load bearing stringer and rail track.

Automatic modulation classification plays an essential role in cognitive communication systems. Traditional automatic modulation classification approaches are primarily developed under Gaussian noise assumptions. Nevertheless, recent empirical studies show that the impulsive noise has emerged in numerous wireless communication systems. The bursty nature of impulsive noise fundamentally challenges the applicability of traditional automatic modulation classification approaches. In order to accurately identify the modulation schemes in impulsive noise environment, in this paper, we propose a novel modulation classification approach through using hyperbolic-tangent cyclic spectrum and multi-branch attention shuffle neural networks. First, based on the designed hyperbolic-tangent autocorrelation function, hyperbolic-tangent cyclic spectrum is proposed to effectively suppress impulsive noise and extract the discriminating features. Then, based on the hyperbolic-tangent cyclic spectrum, a novel deep shuffle neural network is proposed as a classifier to perform the modulation classification through multi-branch attention mechanism to reweight all features. Both numerical and real-data experimental results demonstrate that the proposed algorithm can correctly classify modulation schemes with high accuracy and robustness in impulsive noise.

Woven fabric composite structures are applied in a wide range of industrial applications. Composite structures are vulnerable to damage from working in complex conditions and environments, which threatens the safety of the in-service structure. Damage detection based on Lamb waves is one of the most promising structural health monitoring (SHM) techniques for composite materials. In this paper, based on guided Lamb waves, a lightweight deep-learning approach is proposed for identifying damaged regions in woven fabric composite structures. The designed deep neural networks are built using group convolution and depthwise separated convolution, which can reduce the parameters considerably. The input of this model is a multi-channel matrix transformed by a one-dimensional guided wave signal. In addition, channel shuffling is introduced to increase the interaction between features, and a multi-head self-attention module is designed to increase the model’s global modeling capabilities. The relevant experimental results show that the proposed SHM approach can achieve a recognition accuracy of 100% after only eight epochs of training, and the proposed LCANet has only 4.10% of the parameters of contrastive SHM methods, which further validates the effectiveness and reliability of the proposed method.

Impact monitoring technology plays a critical role in ensuring the structural integrity and safety of thin-walled engineering structures in service. This paper presents a novel hybrid DoA-TDoA method for impact localization on thin-walled engineering structures using sensor clusters. The methodology involves placing two sensor clusters on the structure to capture impact signals. Subsequently, narrowband Lamb wave signals at a specific frequency are extracted from impact signals using Continuous Wavelet Transform (CWT). The Normalized Variance Sequence (NVS) approach is then employed to determine the TDoA, and phase differences are calculated to estimate the Direction of Arrival (DoA). The DoA-based Spatial Beamforming Focusing (SBF) technique and TDoA-based hyperbolic locus imaging algorithm are used for impact imaging. An imaging fusion step is introduced to combine the results of the two imaging techniques, accurately determining the impact location. Experimental validation of the proposed method is conducted through impact tests on three distinct structures: a large-scale plate, a complex riveted stiffened plate, and a three-dimensional thin-walled cylindrical structure. A comparative analysis with two existing methods demonstrates the superior imaging resolution and localization accuracy of the proposed approach, which remains effective even in the presence of measurement noise. Additionally, the effects of sensor type, shape, and configuration on the localization results are discussed. This research contributes to the advancement of impact localization technology for thin-walled structures, with potential applications in structural health monitoring and safety assessment.

Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise.