Sea Trial Data Research Articles

A baseline decomposition ultra-short baseline localization algorithm for arbitrary array structures.

With the rapid development of the marine economy, hydroacoustic positioning technology plays an increasingly important role in marine engineering. The ultra-short baseline (USBL) hydroacoustic positioning system has the advantages of small size, simple operation, and flexible use, and has been widely used. Aiming at the existing USBL acoustic positioning algorithm with low positioning accuracy and complex calculation, a baseline decomposition localization algorithm with arbitrary array structure is proposed. The algorithm is based on the theory of coordinate system transformation, establishes positioning observation equations for each baseline in the base array, and adopts the least squares method to obtain positioning results by selecting different combinations of baselines. The systematic errors of different positioning models themselves are simulated, and then the effects of the three parameter errors, namely, time delay, element coordinates, and sound speed, on the positioning results are analyzed, respectively. Finally, the simulation results and sea trial data show that, compared with the existing algorithms, this algorithm not only simplifies the complicated computation process, but also improves the positioning accuracy and robustness, and has a better application effect.

Wideband beam domain sparse Bayesian learning passive focusing localisation algorithm

AbstractTo address the challenges of large‐aperture sonar systems passive localisation, this paper proposes the application of sparse Bayesian learning (SBL) for passive target localisation in the wideband beam domain. The proposed algorithm aims to overcome the issues of massive computational requirements for two‐dimensional SBL scanning and increased localisation errors due to interference energy leakage. The wideband beam domain SBL focusing localisation algorithm is developed by constructing an azimuth‐range two‐dimensional transformation matrix to preprocess array data, which effectively reduces the computational load of SBL processing while suppressing strong interference energy leakage in passive sonar operating environments, thus improving the range resolution and parameter estimation accuracy of focusing localisation. Simulation and sea trial data analyses demonstrate the feasibility of the proposed algorithm, with results indicating its superior performance compared to existing algorithms.

Off-Grid Underwater Acoustic Source Direction-of-Arrival Estimation Method Based on Iterative Empirical Mode Decomposition Interval Threshold.

To solve the problem that the hydrophone arrays are disturbed by ocean noise when collecting signals in shallow seas, resulting in reduced accuracy and resolution of target orientation estimation, a direction-of-arrival (DOA) estimation algorithm based on iterative EMD interval thresholding (EMD-IIT) and off-grid sparse Bayesian learning is proposed. Firstly, the noisy signal acquired by the hydrophone array is denoised by the EMD-IIT algorithm. Secondly, the singular value decomposition is performed on the denoised signal, and then an off-grid sparse reconstruction model is established. Finally, the maximum a posteriori probability of the target signal is obtained by the Bayesian learning algorithm, and the DOA estimate of the target is derived to achieve the orientation estimation of the target. Simulation analysis and sea trial data results show that the algorithm achieves a resolution probability of 100% at an azimuthal separation of 8° between adjacent signal sources. At a low signal-to-noise ratio of -9 dB, the resolution probability reaches 100%. Compared with the conventional MUSIC-like and OGSBI-SVD algorithms, this algorithm can effectively eliminate noise interference and provides better performance in terms of localization accuracy, algorithm runtime, and algorithm robustness.

Self-Supervised Marine Noise Learning with Sparse Autoencoder Network for Generative Target Magnetic Anomaly Detection

As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. Recently, learning-based detectors have been widely studied for the discrimination of magnetic anomaly signal and achieve superior performance than traditional rule-based detectors. Nevertheless, learning-based detectors require abundant data for model parameter training, which are difficult to access in practical marine applications. In practice, target magnetic anomaly data are usually expensive to acquire, while rich marine magnetic noise data are readily available. Thus, there is an urgent need to develop effective models to learn discriminative features from the abundant marine magnetic noise data for newly appearing target anomaly detection. Motivated by this, in this paper we formulate MAD as a single-edge detection problem and develop a self-supervised marine noise learning approach for target anomaly classification. Specifically, a sparse autoencoder network is designed to model the marine noise and restore basis geomagnetic field from the collected noisy magnetic data. Subsequently, reconstruction error of the network is used as a statistical decision criterion to discriminate target magnetic anomaly from cluttered noise. Finally, we verify the effectiveness of the proposed approach on real sea trial data and compare it with seven state-of-the-art MAD methods on four numerical indexes. Experimental results indicate that it achieves a detection accuracy of 93.61% and has a running time of 21.06 s on the test dataset, showing superior MAD performance over its counterparts.

Self correction technique for amplitude and phase errors of underwater acoustic arrays based on variable decibel Bayesian inference

Abstract Direction of arrival (DOA) estimation of underwater targets is an important research direction in the field of underwater acoustic array signal processing. Due to factors such as assembly errors and component aging, amplitude and phase errors between each channel of the array are common, resulting in distortion of the array flow pattern matrix. At the same time, the non-uniform distribution of noise intensity at the positions of each array element further increases the difficulty of DOA for underwater targets. Based on the characteristics of sparse spatial distribution of underwater targets, this paper uses the flexibility of hyperparametric modeling to construct an array observation signal graph model. On this basis, the effects of amplitude and phase errors and non-uniform noise on the array model are added, and a comprehensive observation model with clear distribution types of amplitude and phase errors, signals, and noise is established. The variational Bayesian inference theory is used to obtain closed solutions of various variables and parameters, so as to achieve joint estimation of target azimuth and error parameters. Simulation results and sea trial data processing results show that the algorithm can achieve amplitude and phase error self correction while completing DOA estimation in the absence of prior information on the number of sources.

A Method of Ship Target Modulation Feature Enhancement Based on Phase Information

Abstract Ship modulation features are commonly used in underwater acoustic target recognition, which contains propeller parameter information of the target. The basis processing of modulation spectrum is demodulation. Traditional envelope demodulation methods only consider amplitude information and make little use of phase information, which can fully characterize the dynamic characteristics of radiated noise. In this paper, a modulation feature enhancement method based on phase information is proposed, which takes full account of the phase information of the target signal. It uses the modified group delay function to demodulate the target signal, and utilizes the phase stability characteristics of the modulated line spectrum to enhance the modulation features of the ship target by weighted phase features. Simulation and sea trial data verification show that the proposed method can effectively enhance the energy of the line spectrum, suppress the background noise, and improve the signal-to-noise ratio of the modulation line spectrum, which lays a good foundation for further using the modulation line spectrum to carry out the estimation of modulation parameters, and has a good application prospect in the field of underwater acoustic target recognition.

Modulation recognition for underwater acoustic communication based on hybrid neural network and feature fusion

It is a huge challenge for underwater acoustic receivers to correctly identify modulation methods due to the complex underwater channel environment and severe noise interference. Combined with the lightweight network (SqueezeNet) and attention mechanism (SENet), a multi-attribute and multi-scale feature fusion model based on a hybrid neural network is proposed, which achieves efficient and accurate recognition for modulation modes. First, the wavelet time-frequency (WTF) spectrum, square power spectrum, and contour maps of cyclic spectrum are extracted as multi-attribute inputs for the network to reduce the impact of inherent defects in single attribute feature. Second, shallow and deep features based on the SqueezeNet model are obtained as multi-scale features, of which the key feature expression ability is enhanced by the SENet model to provide sufficient feature information for modulation recognition. The simulation experiments and sea trial data confirm that the suggested method demonstrates strong generalization capabilities and effectiveness when applied to underwater acoustic channels and environmental noise. In contrast to algorithms in existence, the method verifies superior recognition abilities.

Application research on vector coherent frequency‐domain batch adaptive line enhancement in deep water

AbstractThe low frequency line spectrum noise radiated by ships has strong stability and is difficult to eliminate, which is the key information required for passive signal detection. A vector coherent frequency‐domain batch adaptive line enhancement method is proposed to address the issue of insufficient detection capability of traditional scalar adaptive line enhancement (ALE) algorithms for ship characteristic line spectra in complex deep‐sea environments. This method not only introduces the idea of frequency‐domain batch processing, but also uses synchronously collected sound pressure and particle velocity as dual input, fully utilising the coherence characteristics between vector channels to output high gain line spectrum signals and improve computational efficiency. In simulation and sea trial data validation, compared with the time‐domain vector coherent adaptive line enhancement algorithm, this method has shorter time consumption, higher efficiency, and can improve the detection ability of line spectrum signals under low signal‐to‐noise ratio conditions. The bearing estimation results output by this algorithm is also more accurate.

Research on Sea Trial Techniques for Motion Responses of HDPE Floating Rafts Used in Aquaculture

The innovative aquaculture equipment known as high-density polyethylene (HDPE) floating rafts has gained popularity among fishermen in the southeast coastal regions of China. Compared to deep-water anti-wave fish cages, the construction costs of HDPE floating rafts are 50% to 75% less. There is a dearth of comprehensive publicly available records of HDPE floating rafts sea trial data, despite substantial numerical studies on the motion response of aquaculture fish cages and scale model experiments under controlled-wave conditions. This study involves sea trial techniques under operational and extreme environmental conditions for motion responses of HDPE floating rafts, presents a comprehensive procedure for sea trials of HDPE floating rafts, summarizes the issues encountered during the trials, and suggests solutions. Using MATLAB for independent programming, motion videos and photos collected from the sea trials are processed for image capture, yielding the original time history curve of vertical displacement. Based on the sea trials’ data, including motion displacement, acceleration, mooring line force, overall deformation patterns, and current and wave data, recommendations are provided for the design and layout of HDPE floating rafts. Based on the Fast Fourier Transform (FFT) method for spectral analysis, the influence of interference items on the observational data is eliminated; the rationality of the observational data is verified in conjunction with the results of the Gabor Transform. This study offers a scientific analytical method for the structural design and safe operation of HDPE floating rafts and provides a reference for subsequent numerical simulations.

A differential-based method for correcting ultra-short baseline positioning results

The primary factors influencing the positional accuracy of an ultra-short baseline (USBL) system are errors in sound speed, delay, installation deviation, and the bending of sound rays. Premised on the USBL positioning model, an error contribution model including ranging error, delay error, sound speed error and array element installation deviation was constructed. In parallel, an indirect difference correction method with additional coefficients was proposed based on the geometric correlation of the error function model. Specifically, a reference beacon was deployed on the seabed, and differential correction was achieved by directly computing target coordinates, integrating them with the positional data from the reference beacon. The proposed method demonstrated efficacy in mitigating errors associated with gradual variations or pronounced geometric correlations, including discrepancies in sound speed, bending of sound rays, and deviations in the installation of array elements. The correction method was applied to the USBL positioning, and the simulation results show that the proposed method notably enhanced both accuracy and consistency in positioning, particularly in shallow water environments characterised by significant sound speed errors. The sea trial data from the R/V Dayang Yihao in the Western Pacific demonstrate that the proposed method can effectively mitigate the errors induced by sound ray bending in deep-sea environments. The mean absolute error (MAE) and standard deviation (STD) were reduced by 51.3% and 55.3%, respectively, compared with the results of the USBL direct solution.

Doppler and Channel Estimation Using Superimposed Linear Frequency Modulation Preamble Signal for Underwater Acoustic Communication

Due to the relative motion between transmitters and receivers and the multipath characteristic of wideband underwater acoustic channels, Doppler and channel estimations are of great significance for an underwater acoustic (UWA) communication system. In this paper, a preamble signal based on superimposed linear frequency modulation (LFM) signals is first designed. Based on the designed preamble signal, a real-time Doppler factor estimation algorithm is proposed. The relative correlation peak shift of two LFM signals in the designed preamble signal is utilized to estimate the Doppler factor. Moreover, an enhanced channel estimation algorithm, the correlation-peak-search-based improved orthogonal matching pursuit (CPS-IOMP) algorithm, is also proposed. In the CPS-IOMP algorithm, the excellent autocorrelation characteristic of the designed preamble signal is used to estimate the channel sparsity and multipath delays, which are utilized to construct the simplified dictionary matrix. The simulation and sea trial data analysis results validated the designed preamble, the proposed Doppler estimation algorithm, and the channel estimation algorithm. The performance of the proposed Doppler factor estimation is better than that of the block estimation algorithm. Compared with the original OMP algorithm with known channel sparsity, the proposed CPS-IOMP algorithm achieves a similar estimation accuracy with a smaller computational complexity, as well as requiring no prior knowledge about the channel sparsity.

Motion states identification of underwater glider based on complex networks and graph convolutional networks

Underwater glider (UG) plays an important role in ocean observation and exploration for a more efficient and deeper understanding of complex ocean environment. Timely identifying the motion states of UG is conducive for timely attitude adjustment and detection of potential anomalies, thereby improving the working reliability of UG. Combining limited penetrable visibility graph (LPVG) and graph convolutional networks (GCN) with self-attention mechanisms, we propose a novel method for motion states identification of UG, which is called as visibility graph and self-attention mechanism-based graph convolutional network (VGSA-GCN). Based on the actual sea trial data of UG, we chose the attitude angle signals of motion states related sensors collected by the control system of UG as the research object and constructed complex networks based on the LPVG method from pitch angle, roll angle, and heading angle data in diving and climbing states. Then, we build a self-attention mechanism-based GCN framework and classify the graphs under different motion states constructed by a complex network. Compared with support vector machines, convolutional neural network, and GCN without self-attention pooling layer, the proposed VGSA-GCN method can more accurately distinguish the diving and climbing states of UG. Subsequently, we analyze the variation of the transitivity coefficient corresponding to these two motion states. The results suggest that the coordination of the various sensors in the attitude adjustment unit during diving becomes closer and more efficient, which corresponds to the higher network measure of the diving state compared to the climbing state.

MTSA-Net: A multiscale time self-attention network for ship radiated self-noise reduction

For a surface ship carrying a towed array, its in-band radiated self-noise is one of the near-field strong interference, which will seriously limit the performance of the underwater acoustic (UWA) signal detection system. Typically, the conventional ship noise cancellation methods requires time-synchronized hydrophone arrays, such as the towed linear array (TLA), to suppress its self-noise by using spatial filters. However, the spatial filter based methods fail when the direction of arrival of the desired signal is in the ship noise masking area. In cooperative scenarios, the prior knowledge of the template signal provides additional temporal information, which can be utilized to design a time-domain representations based detection system. In this paper, a multiscale time self-attention network (MTSA-Net) is proposed to mitigate the ship radiated self-noise and enhance the desired signal to improve the performance of signal detection system. Experimental results based on sea trial data indicate the effectiveness of our proposed method.

Bearing‐only motion analysis of target based on low‐quality bearing‐time recordings map

AbstractAccurate bearing‐time recordings play a crucial role in hydroacoustic target motion analysis and situation estimation. The target bearing estimated by vector hydrophone in the interference environment suffers from poor display and low signal‐to‐noise ratio. To solve this problem, a two‐stage bearing‐only motion analysis method is proposed. Firstly, the bearing peak information and bearing dispersion are extracted for each sampling moment of the bearing‐time recording. Custom constraint regions and adaptive parameters are then set based on this information. The order truncate average algorithm is improved to enhance the quality of the low‐quality bearing‐time recording map. Secondly, a bearing‐only target motion model is established based on the bearing‐time recording distribution features, and a bearing‐time recording trust interval is constructed. Then a modified pseudo‐linear estimation algorithm is proposed to solve the target motion situation information, including target abeam time, target abeam bearing, target course, velocity‐to‐initial‐distance ratio, and so forth. Finally, by comparing the solved values of the sea trial data with the automatic identification system calculation results, the enhancement effect of the target bearing‐time recording map is significant and the solving error of the target motion elements meets the needs of engineering applications.

Real-Time Underwater Acoustic Homing Weapon Target Recognition Based on a Stacking Technique of Ensemble Learning

Underwater acoustic homing weapons (UAHWs) are formidable underwater weapons with the capability to detect, identify, and rapidly engage targets. Swift and precise target identification is crucial for the successful engagement of targets via UAHWs. This study presents a real-time target recognition method for UAHWs based on stacking ensemble technology. UAHWs emit active broadband detection signals that manifest distinct reflection characteristics on the target. Consequently, we have extracted energy and spatial distribution features from the target’s broadband correlation detection output. To address the problem of imbalanced original sea trial data, we employed the SMOTE algorithm to generate a relatively balanced dataset. Then, we established a stacking ensemble model and performed training and testing on both the original dataset and relatively balanced dataset separately. In conclusion, we deployed the stacking ensemble model on an embedded system. The proposed method was validated using real underwater acoustic homing weapon sea trial data. The experiment utilized 5-fold cross-validation. The results indicate that the method presented in this study achieved an average accuracy of 93.3%, surpassing that of individual classifiers. The model’s single-cycle inference time was 15 ms, meeting real-time requirements.

An improved parameter estimation of HFM signals based on IRLS linear fitting of extracted group delay

To address the issue of robust parameter estimation of hyperbolic frequency modulated (HFM) signals under low signal-to-noise ratios (SNRs), an improved algorithm based on iteratively reweighted least squares (IRLS) linear fitting of extracted group delay is proposed. The algorithm includes three main steps. First, a novel method for estimating the frequency distribution range (FDR) of an HFM signal is presented, which involves analyzing the transient features of the amplitude spectrum (AS). Second, the spectrum phase (SP) is unwrapped in the estimated FDR to extract the group delay (GD) of the HFM signal. Last, parameter estimation of the HFM signal is achieved by fitting the GD, which is a linear function of the reciprocal of the frequency, using the IRLS linear fitting method. The simulation results have shown that for SNR ≥ -14 dB, the normalized root mean square errors (NRMSEs) estimated by the proposed estimator are 1.5 dB lower than those estimated by the spectrum phase unwrapping (SPU) estimator, including the starting frequency and period slope. Furthermore, experimental results based on sea trial data have also verified the validity and feasibility of the proposed estimator.

A Robust Denoised Algorithm Based on Hessian–Sparse Deconvolution for Passive Underwater Acoustic Detection

Digital beamforming techniques find wide applications in the field of underwater acoustic array signal processing. However, their azimuthal resolution has long been constrained by the Rayleigh limit, consequently limiting their detection performance. In this paper, we propose a novel two-dimensional Hessian–sparse deconvolution algorithm based on image processing techniques. This method assumes a priori that the underwater acoustic bearing time record (BTR) images exhibit sparsity, and then it first constructs partial differential equations in the beamforming domain with sparsity-norm constraints for optimal noise reduction. Subsequently, a two-dimensional deconvolution operation is applied to narrow the main lobe, aiming to achieve additional temporal gains in two-dimensional processing. The simulation and real sea trial data processing results show that the main lobe width of the proposed method is about 1.3 degrees at 0 dB. It effectively reduces the main lobe width and enhances the detection resolution of BTRs in the post-processing part, especially in low-signal-to-noise-ratio (SNR) environments. Therefore, the proposed method provides nice opportunities to further improve the target-detecting ability of hydrophone arrays.

Unsupervised fault diagnosis framework for underwater thruster system using estimated torques and multi-head convolutional autoencoder

The thruster system is one of the most critical parts of an autonomous underwater vehicle. Feedback, propeller, motor and driver faults are four common field faults of thruster systems. This paper investigates an unsupervised framework based on torques and multi-head convolutional autoencoders for the mentioned faults. The proposed method utilizes an extended state observer to estimate the motor load. The propeller torque is estimated using a physics-guided neural network. Meanwhile, the relationship between the patterns change among torques (motor load and propeller torque) and different faults are analyzed. Then, to avoid feature aliasing, two independent encoders of the multi-head convolutional autoencoder extract the pattern features between the torques automatically. Finally, faults are detected and classified based on the various patterns of the extracted torque features. The sea trial data verified the effectiveness of the scheme. The results show that the method can accurately detect and classify five specific faults: current feedback fault, speed feedback fault, propeller loss, propeller winding and open-circuit fault.

Passive ranging of a moving target in the direct-arrival zone in deep sea using a single vector hydrophone.

The vector hydrophone covers more comprehensive sound field information over the sound pressure hydrophone. The passive target ranging method based on the shallow-located unmanned platform equipped with vector hydrophones is explored based on the measured surface ship data acquired in the deep-sea area in December 2021. First, a bearings-only target motion analysis (TMA) algorithm is proposed, determining the motion state of a moving target (e.g., azimuth angle, abeam time, heading angle, and the ratio of velocity to initial distance) in accordance with its horizontal bearing time recording (BTR) data. Subsequently, the loss function is built following the situation information and the average complex acoustic intensity to determine the real-time grazing angle of the target. Last, the real-time range of the target is determined in accordance with the situation information and the grazing angle. As revealed by the comparison of the calculated value of the sea trial data with the GPS trajectory, the average distance error of the target reaches 11.6%, the root mean square error (RMSE) of the grazing angle is 1.99°, the RMSE of the velocity is 1.9 kn, and the RMSE of the abeam time reaches 51.4 s.

Application of trans-dimensional particle filtering for geoacustic inversion

The acquiring geoacoustic model and associated geoacoustic parameters is vital for sound propagation modelling in a range-dependent environment. Conventional sequential methods, e.g., particle filtering and Kalman filtering have been widely used for geoacoustic inversion in range-dependent environment, which encounter challenges when confronted with unknown geoacoustic models that intrinsically varying with range. As an attempt to estimate geoacoustic model and associated geoacoustic parameters as well, a trans-dimensional particle filtering method is presented here. This method integrates birth-death rules into the filtering process, enabling automatic selection of appropriate geoacoustic models and estimating geoacoustic parameters in the same time. Numerical simulations were conducted based on an environment model of the South China Sea, where a sea trial was conducted in 2022. Numerical results demonstrate the efficiency in accurately estimating the geoacoustic model variations and associated parameters. Preliminary results of sea trial data processing are also presented here.