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Abstract Underwater sensor networks hold immense potential for advancing the field of underwater target tracking, yet they encounter significant resource constraints stemming from energy storage and communication methods. In order to balance tracking accuracy and energy consumption, the authors present a distributed bearing‐only target tracking algorithm that can be used in underwater sensor networks with resource constraints. Anchored in the diffusion cubature information filter framework, this algorithm achieves fusion for non‐linear bearing measurements and state estimation. During the incremental update stage, individual nodes leverage the Posterior Cramer‐Rao Lower Bound as a metric for tracking performance. Subsequently, a strategy for selecting neighbouring nodes is introduced, ensuring tracking accuracy while efficiently kerbing energy consumption. In the diffusion update stage, a multi‐threshold event triggering mechanism is employed to partially diffuse the intermediate estimation. Additionally, an adaptive convex combination weight is proposed for cases involving partial diffusion. Through theoretical analysis, the asymptotic unbiasedness and convergence of the algorithm have been proven. Through Monte Carlo simulation experiments, the authors verify that the algorithm is superior to existing algorithms. Furthermore, the algorithm significantly reduces energy consumption in information interaction, minimising tracking accuracy loss.

As a kind of classical low-frequency sound-absorbing material, the microperforated plate (MPP) has been widely used. Here, we inspired by the sound absorption mechanism of the MPP, a spiral metasurface (SM) is designed and the analytical solution of acoustic impedance and sound absorption coefficient are obtained. The relationship between the sound absorption properties of the MPP and the SM with their own structures is systematically studied, and the analytical solutions are used to optimise the structure. It is concluded that the MPP and the SM of the same thickness achieve effective absorption in the frequency range between 390-900 Hz and 1920-4266 Hz, with a total thickness less than 1/6 of the wavelength. Meanwhile, the numerical calculation shows that the MPP and SM can match well with the background medium in the effective rang. Our study provides new insights into the design methods of sound-absorbing materials and is potentially suitable for many acoustic engineering applications.

Abstract Pattern synthesis of the sparse linear array (SLA) has played an important role when the antenna size is extremely limited. Although grid‐based compressed sensing (CS) algorithms have been widely utilised to synthesise the SLA, the performance is greatly affected by the grid mismatch problem. To solve the problem, a reweighted gridless CS (RGCS) algorithm based on the reweighted atomic norm minimisation and the rotational invariance propagator method is introduced. In the RGCS algorithm, the number of antenna elements can be efficiently reduced through the reweighted gridless convex optimisation, which utilises a reweighted strategy to break the limit of the atomic norm and improves the performance of the SLA. More importantly, in addition to the focussed‐beam pattern, the proposed algorithm can also synthesise the SLA with the asymmetric beam pattern. Numerical experiments show that the RGCS algorithm can save about 18.75%–46% array elements for the uniform linear array.

Dolphin signals are effective carriers for underwater covert detection and communication. However, the environmental and cost constraints terribly limit the amount of data available in dolphin signal datasets are often limited. Meanwhile, due to the low computational power and resource sensitivity of Unmanned Underwater Vehicles (UUVs), current methods for real-time generation of dolphin signals with favorable results are still subject to several challenges. To this end, a Masked AutoEncoder Generative Adversarial Network (MAE-GAN) model is hereby proposed. First, considering the few-shot condition, the dataset is extended by using data augmentation techniques. Then, to meet the low arithmetic constraint, a denoising autoencoder with a mask is used to obtain latent codes through self-supervised learning. These latent codes are then utilized in Conditional Wasserstein Generative Adversarial Network-Gradient Penalty (CWGAN-GP) to generate a whistle signal model for the target dataset, fully demonstrating the effectiveness of the proposed method for enhancing dolphin signal generation in data-limited scenarios. The whistle signals generated by the MAE-GAN and baseline models are compared with actual dolphin signals, and the findings indicate that the proposed approach achieves a discriminative score of 0.074, which is 28.8% higher than that of the current state-of-the-art techniques. Furthermore, it requires only 30.2% of the computational resources of the baseline model. Overall, this paper presents a novel approach to generating high-quality dolphin signals in data-limited situations, which can also be deployed on low-resource devices. The proposed MAE-GAN methods provide a promising solution to address the challenges of limited data and computational power in generating dolphin signals.

Active sonar systems are one of the most commonly used acoustic devices for underwater equipment. They use observed signals, which mainly include target echo signals and reverberation, to detect, track, and locate underwater targets. Reverberation is the primary background interference for active sonar systems, especially in shallow sea environments. It is coupled with the target echo signal in both the time and frequency domain, which significantly complicates the extraction and analysis of the target echo signal. To combat the effect of reverberation, an attention and cepstrum analysis-guided network (ACANet) is proposed. The baseline system of the ACANet consists of a one-dimensional (1D) convolutional module and a reconstruction module. These are used to perform nonlinear mapping and to reconstruct clean spectrograms, respectively. Then, since most underwater targets contain multiple highlights, a cepstrum analysis module and a multi-head self-attention module are deployed before the baseline system to improve the reverberation suppression performance for multi-highlight targets. The systematic evaluation demonstrates that the proposed algorithm effectively suppresses the reverberation in observed signals and greatly preserves the highlight structure. Compared with NMF methods, the proposed ACANet no longer requires the target echo signal to be low-rank. Thus, it can better suppress the reverberation in multi-highlight observed signals. Furthermore, it demonstrates superior performance over NMF methods in the task of reverberation suppression for single-highlight observed signals. It creates favorable conditions for underwater platforms, such as unmanned underwater vehicles (UUVs), to carry out underwater target detection and tracking tasks.