Gaussian/non-Gaussian impulsive noises in underwater acoustic (UWA) channel seriously impact the quality of underwater acoustic communication. The common denoising algorithms are based on Gaussian noise model and are difficult to apply to the coexistence of Gaussian/non-Gaussian impulsive noises. Therefore, a new UWA noise model is described in this paper by combining the symmetric α-stable (SαS) distribution and normal distribution. Furthermore, a novel underwater acoustic signal denoising algorithm called AWMF+GDES is proposed. First, the non-Gaussian impulsive noise is adaptively suppressed by the adaptive window median filter (AWMF). Second, an enhanced wavelet threshold optimization algorithm with a new threshold function is proposed to suppress the Gaussian noise. The optimal threshold parameters are obtained based on good point set and dynamic elite group guidance combined simulated annealing selection artificial bee colony (GDES-ABC) algorithm. The numerical simulations demonstrate that the convergence speed and the convergence precision of the proposed GDES-ABC algorithm can be increased by 25%~66% and 21%~73%, respectively, compared with the existing algorithms. Finally, the experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that both the proposed wavelet threshold optimization method based on GDES-ABC and the AWMF+GDES algorithm can obtain higher output signal-to-noise ratio (SNR), noise suppression ratio (NSR), and smaller root mean square error (RMSE) compared with the other algorithms.

With the development of the Internet of Underwater Things (IoUT), both academia and industry have significant emphasized underwater wireless sensor networks (UWSNs). To address the issues of slow convergence, high latency, and limited energy in existing intelligent routing protocols in UWSNs, a cross-layer opportunistic routing protocol (CLORP) for underwater sensor networks based on multi-agent reinforcement learning (MARL) is proposed in this paper. First, CLORP combines the decision-making capability of multi-agent reinforcement learning with the idea of opportunistic routing to sequentially select a set of neighbors with larger values as potential forwarding nodes, thereby increasing the packet transmission success rate. Second, in the design of the MARL reward function, two reward functions for successful and unsuccessful packet transmission are designed jointly with cross-layer information to improve the routing protocol’s performance. Finally, two algorithmic optimization strategies, adaptive learning rate and Q-value initialization based on location and number of neighbors, are proposed to facilitate the faster adaptation of agents to the dynamic changes of network topology and accelerate CLORP convergence. The experimental results demonstrate that CLORP can increase algorithm convergence speed by 13.2%, reduce network energy consumption by 25%, and decrease network latency by 31.2%.

Traditional beamforming (BF) methods can be used to suppress the Gaussian interferers in underwater acoustic communication systems. However, multiple unsteady sub-Gaussian interferers may exist that degrade the performance of traditional BF. In this paper, a new beamformer is proposed for the suppression of multiple unsteady sub-Gaussian interferers (SMUSGI). The proposed BF combines the multiplier and the quasi-Newton methods to obtain the optimal weight vector. Simulation experiments are conducted to validate the performance of the proposed beamformer. In addition, the proposed beamformer is compared with the traditional methods including the MVDR, Subspace, Bartlett, MDDR, LCMD and LCMV beamformers. The obtained results demonstrate that the proposed beamformer can effectively suppress multiple unsteady sub-Gaussian interferers with a higher output signal-to-interference-plus-noise ratio (SINR), and faster convergence compared with the traditional methods.

It has been recognized that the performance of the traditional direction of arrival (DOA) estimation algorithms is highly dependent on array geometry parameters, especially the array size and element number. Unfortunately, significant performance degradation may be caused under unideal array geometry such as small size array onboard small underwater vehicle. Among all the possible directions, the presence of underwater target at a certain direction constitutes a type of spatial sparsity. Thus, spatial sparse recovery provides a potential way to address this challenging problem. As each DOA will correspond to a different transmission response pattern with respect to each array element, thus constitutes a direction-dependent transmission response (DDTR) that can be explored. However, random contribution of underwater acoustic (UWA) channel contained in DDTR makes it extremely difficult to directly construct dictionary matrix with DDTR. In this paper, a novel DDTR based small size array DOA estimation method is proposed. First, DDTRs are decomposed into time delay (TD) term and random channel response (RCR) term to decouple the adverse impact of UWA channel. Second, TD is conveniently constructed as dictionary matrix in terms of geometry relationship. Then, by accumulating the constructed DOA at frequency bins selected with energy or correlation coefficient, a selective frequency accumulation compressed sensing reconstruction (SFA-CS) algorithm is designed to mitigate the negative effect of UWA channel. Compared with the conventional DOA estimation algorithms, simulation results demonstrate that the proposed method can achieve small array DOA estimation with a lower root mean squared error (RMSE) and higher success rate (SR).

Sound source localization provides an absorbing capability for unmanned aerial vehicles (UAVs) in scenarios such as search and rescue operations. The shape fusion between the sound array and UAVs forms a special conformal property that is drawing more and more attention. However, the inevitable shadow effect caused by shape fusion seriously degrades the degrees of freedom (DOF) of the array. In this paper, a signal reconstruction-based direction of arrival (DOA) estimation method is proposed to address this limitation. Firstly, we establish a restricted signal model for the conformal cylindrical array (CCA), and then based on frequency domain energy detection, the elements are divided into receiving restricted elements and receiving normal elements. Secondly, according to the position vector of receiving restricted elements, the approximate range of the DOA is roughly estimated to reduce the complexity. Meanwhile, the signals of receiving restricted elements are reconstructed on the basis of receiving normal elements to eliminate the shadow effect and increase the DOF. Lastly, in the estimated approximate range of the DOA, the precise DOA is estimated by peak search. We also derive the 2-D Cramer-Rao lower bound (CRLB) for the CCA. Simulations show that the proposed SR-MUSIC-RS method can achieve satisfactory performance with lower complexity, and the RMSE is close to that of general signal model with normal elements.

In this letter, a metastructure (MS)-enabled underwater acoustic (UWA) integrated sensing and communication (ISAC) approach is investigated. Firstly, a hemisphere metastructure with multiple series-connection pore-cavity structures is designed to construct direction-dependent regulation patterns, which is equivalent to assigning different spatial-spectral regulation responses along different directions of the MS. Then, received UWA signal entering the MS from different directions is spatially regulated by different MS-driven regulation patterns, thus enabling sensing of direction of arrival (DOA) via detection among candidate regulation patterns. Upon the obtaining of DOA, a multiple metastructure-driven pattern shift keying (MMPSK) modulation and demodulation scheme are designed to regulate the signal with regulation patterns selected according to the information bits from the pattern set that obtained in direction-wise anechoic tank test in advance, and then recover the information bits by detecting the regulation patterns at receiver. Meanwhile, to overcome the impact of random UWA channel, peak characteristic of autocorrelation function of transmitted ISAC signals and multipath characteristic of UWA channel are simultaneously designed for DOA sensing and communication. Lake experiments confirm the effectiveness of the proposed system.