Cong Peng

This study investigates how ocean fronts affect underwater sound propagation in the shallow waters surrounding Hainan Island. To characterize the regional environment, HYCOM oceanographic fields and ETOPO1 global bathymetry are transformed from geographic coordinates into Lambert conformal conic (LCC) coordinates to construct a regional environmental database. Building on this database, a Vertical Depth Fine Interpolation Method (VD-FIM) is developed to refine sound-speed profiles and to generate an ocean-acoustic dataset for sound-field simulations. Using this dataset, transmission-loss calculations indicate that seasonal variability, regional circulation, and atmospheric forcing drive strong interactions between cold and warm water masses. These interactions form pronounced fronts that add about 10–20 dB of transmission loss and significantly modify the shallow-water waveguide. In autumn and winter, weaker vertical sound-speed gradients are more favorable for long-range propagation, and sources placed in cold-water masses exhibit improved detection performance at long ranges. Acoustic measurements collected in the South China Sea in November 2023 are consistent with the modeled transmission loss. This consistency indicates that the proposed environmental-modeling and data-processing framework can quantify front-induced acoustic effects and guide the selection of detection periods and source locations. Code for this study is available at GitHub: https://github.com/jxk2026/Hainan-Shallow-Water-Acoustics.

Although beamforming design enables port-starboard discrimination to some extent, it performs poorly under low signal-to-noise ratio (SNR) conditions and exhibits low port-starboard suppression, leading to potential misclassification. To address the challenge of port-starboard discrimination in underwater acoustic detection using dual linear arrays, this paper proposes a novel discrimination algorithm based on dual linear array processing. The proposed algorithm first utilizes the Linearly Constrained Minimum Variance (LCMV) criterion to construct a beamforming vector and calculate the spatial spectrum of the received data, thereby obtaining a coarse estimation of the target's azimuth. Building on the beamforming process, we construct a dictionary matrix to establish the relationship between the spatial spectrum and the spatial power distribution vector. Furthermore, to mitigate the impact of sidelobes in the spatial spectrum, we design a set of projection operators to process the spatial spectrum and dictionary matrix, thereby obtaining a cleaner spatial relationship. In addition, to enhance numerical stability, we incorporate a regularization term into the objective function using the noise subspace. Simulations and experimental demonstrate that the proposed algorithm achieves an exceptionally high port-starboard suppression ratio and superior azimuth resolution capability.

The background of the active sonar deviates far from the Rayleigh model when encountering reverberation or clutter, which causes unexpected false alarm rates in conventional detectors. Especially for the low-frequency active sonar, due to its relatively low measurement resolution, there are relatively few features that can be utilized for target recognition. In this article, a Joint-Distribution (Joint-D) based fitting method is proposed for non-Rayleigh sonar data, to enhance the performance of conventional constant false alarm rate (CFAR) detection. Subsequently, other than single energy criterion, multi feature fusion based on Joint-D is implemented for both the local and spatial subregions of the detection results. Finally, target recognition is performed using a classifier trained by dual-stage support vector machine (SVM), where the first stage utilizes local features and the second stage utilizes spatial features. Receiver operating characteristic (ROC) curves show that the proposed method outperforms conventional counterparts across SRR from 0-25 dB. When achieving the same detection rate, particularly the 90% threshold, it requires an average of 5 dB less SRR than conventional counterparts. In the case study using sea trial data, the detection rate reaches 100%, and the false alarm rate is consistent with the theoretical value.

Deconvolution algorithms exhibit significant limitations in complex environments. To enhance the accuracy of direction-of-arrival (DOA) estimation, this article proposes an estimation algorithm that integrates deep learning with deconvolution processing. The algorithm preprocesses the received data using a deconvolution approach and employs quadratic programming and compressed sensing algorithms to obtain the spatial power distribution vector and the input data for the neural network. To improve the accuracy of covariance matrix estimation, a stacked convolutional autoencoder model is constructed, which suppresses noise in the covariance matrix through model pretraining. In addition, we design a data selection operation to enhance the network’s focus on target locations. Furthermore, an attention mechanism tailored for beamforming results is introduced, utilizing multiple attention taps and bias taps to process data, enhancing the accuracy of error estimation. By integrating the attention mechanism with a multimodal network, the proposed algorithm achieves high-precision DOA estimation. Simulation and experimental results validate that the algorithm demonstrates superior estimation accuracy and resolution.

By transferring external information between the equalizer and the decoder iteratively, the performance of turbo equalization is close to the channel capacity. Conventional turbo equalization (CTE) relies on channel coding, while the transmission of external information is a problem in an uncoded system, and turbo equalization without channel coding (TECC) remains unexplored. Therefore, this paper introduces a TECC framework with a virtual decoder constructed by bidirectional processing. The main innovation is that the existence of the virtual decoder enables the transmission of external information. Under this new framework, we implement it with a minimum mean square error decision feedback equalizer (MMSE-DFE) and evaluate its performance across stationary channels and multipath fading channels. Simulation results demonstrate significant communication performance enhancement after three to four iterations, surpassing both conventional bidirectional and unidirectional equalization. In addition, the proposed TECC is verified through underwater acoustic communication in a sea trial. The results also demonstrate that the TECC achieves better bit error performance.

Deconvolution algorithms often rely on conventional beamforming methods to obtain beamforming vectors, which limit their resolution. To enhance parameter estimation resolution, this article introduces the Parameter Estimation by Alternating Reconstruction and Sensation (PEARS) algorithm. In the proposed algorithm, direction estimation leverages a linearly constrained quadratic programming method and weighted L1-norm to solve the objective function, achieving higher resolution in the direction spectrum under fixed weighted vector conditions. The algorithm utilizes the gradient descent method to update the weighted vector, and the relationship among the dictionary matrix, direction spectrum, and weighted vector is computed using the chain rule. This process improves direction estimation results, particularly in scenarios with low signal-to-noise ratios. By alternating between target parameter estimation and weight vector calculation, the PEARS algorithm achieves highly accurate target azimuth estimation. Simulation results validate the algorithm's ability to accurately estimate target azimuth angles. In addition, lake and sea experimental results demonstrate the algorithm's effectiveness in correctly estimating direction in complex environments.

This article proposes a new active sonar detector based on a beamformed deep neural network (BDNN) in three steps. The process involves a preprocessing step, a deep neural network (DNN) application step, and a subsequent postprocessing step. In the preprocessing step, partial spectra are extracted from multiple directions through frequency-domain beamforming. These partial spectra from different directions serve as DNN input, yielding estimated target probabilities as output in the DNN application step. In the postprocessing step, a multiframe probability multiplication technique is proposed, and the number of frames is determined adaptively. The proposed BDNN generates a gridded azimuth-distance graph, where each grid cell represents the probability of a target's presence at a specific azimuth and distance. To guarantee real-time application, we also propose a graphics processing unit based parallel acceleration method, which increases the computation speed of the beamforming process by nearly two orders of magnitude compared to the CPU. The proposed BDNN is verified through sea and lake trials. The results demonstrate that the proposed BDNN achieves better detection performance compared to the conventional matched filter method and exhibits remarkable generalization capabilities.

A wideband Doppler Effect is a significant challenge for underwater acoustic communications (UAC). This paper proposes a new two-stage structure of direct adaptive multi-resampling turbo equalizer (DAM-TEQ) for solving the problem of large timescale errors in time-varying channels, which uses an innovative adaptive time-domain resampling method for Doppler estimation and compensation. In this equalizer, the received signal is first fed into the first-stage structure, in which an adaptive resampling is performed using equalization coefficient detection to achieve a Doppler rough estimation. After the processing is completed, it is fed into the second-stage structure for joint equalization and decoding, effectively reducing the error of information transmission. Compared with the conventional turbo equalizer (TEQ) based on timescale estimation, the proposed equalizer can avoid the problem of the Doppler Effect not being accurately estimated in time-varying channels, with only a slight increase in complexity. Simulations and lake trails show that the equalizer can effectively perform a Doppler estimation and compensation in time-varying channels, and has a better bit error rate (BER) performance than the traditional timescale-based TEQ.

The beamforming obtains the azimuth estimation of the target by extracting the information from the data covariance matrix. However, the beamforming has much-unextracted information in the covariance matrix. Therefore, this paper proposes the Parameter Estimation via Dictionary Subspace Projection (PEDSP) algorithm that further extracts information from the covariance matrix. Through the regularization constraint, the spatial power distribution is limited to the signal subspace to ensure the correctness of the target estimation. In addition, the weighted is used to constrain the spatial power distribution vector to ensure that the targets in the space remain sparse. In the case of the above two constraints, the beamforming results are fitted by a dictionary, and the azimuth estimation of the target is obtained by quadratic programming. The experimental results show that the proposed algorithm can estimate the target azimuth despite the inaccurate sparse constraint. Furthermore, sea trial show that PEDSP can still get correct results in complex environments.