Search Results (13)

Accurate estimation of underwater sound source depth plays a crucial role in ocean acoustic monitoring, underwater target localization, and marine environment exploration. This study exploits the capability of vector hydrophones to simultaneously and co-locally acquire both scalar and vector components of the underwater sound field. Based on the study of the line spectrum interference structure characteristics of the underwater sound field, the vertical sound intensity flux of the underwater sound source is extracted. Additionally, a parallel BiLSTM and ResNet network structure is proposed to train this feature and achieve depth estimation of underwater sound sources. Experimental results show that under ±10% and ±15% errors in the source–hydrophone distance, the proposed model maintains stable performance within a signal-to-noise ratio (SNR) range of −15 dB to +15 dB. Compared with the LSTM model, the ResNet model, and the matched-field processing (MFP) algorithm, the average RMSE of our model is reduced by 72.4%, 54.0%, and 64.1%, respectively. Furthermore, under 5% and 10% frequency estimation errors, the average RMSE of the proposed model within the same SNR range is reduced by 47.7%, 20.3%, and 79.3%, respectively. It effectively estimates the depth of underwater sound sources, with estimation errors below 5 m under non-extreme SNR conditions. These results fully demonstrate the robustness and effectiveness of the proposed method under practical uncertainties in the ocean environment. Full article

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. Full article

This paper studies how intense internal waves (IIWs) affect the holographic reconstruction of the sound field generated by a moving source in a shallow-water environment. It is assumed that the IIWs propagate along the acoustic path between the source and the receiver. The presence of IIWs introduces inhomogeneities into the waveguide and causes significant mode coupling, which perturbs the received sound field. This paper proposes the use of holographic signal processing (HSP) to eliminate perturbations in the received signal caused by mode coupling due to IIWs. Within the HSP framework, we examine the interferogram (the received sound intensity distribution in the frequency–time domain) and the hologram (the two-dimensional Fourier transform of the interferogram) of a moving source in the presence of space–time inhomogeneities caused by IIWs. A key finding is that under the influence of IIWs, the hologram is divided into two regions that correspond to the unperturbed and perturbed components of the sound field. This hologram structure enables the extraction and reconstruction of the interferogram corresponding to the unperturbed field as it would appear in a shallow-water waveguide without IIWs. Numerical simulations of HSP application under the realistic conditions of the SWARM’95 experiment were carried out for stationary and moving sources. The results demonstrate the high efficiency of holographic reconstruction of the unperturbed sound field. Unlike matched field processing (MFP), HSP does not require prior knowledge of the propagation environment. These research results advance signal processing methods in underwater acoustics by introducing efficient HSP methods for environments with spatiotemporal inhomogeneities. Full article

Real-time and accurate construction of regional sound speed profiles (SSPs) is important for building underwater positioning, navigation, and timing (PNT) systems as it greatly affects signal propagation modes. In this paper, we summarize and analyze the current research status in the field of underwater SSP construction, where the mainstream methods include direct SSP measurement and SSP inversion. For the direct measurement method, we compare the performance of popular international and commercial brands of temperature, conductivity, and depth profilers (CTDs). For the inversion methods, the framework and basic principles of matched field processing (MFP), compressive sensing (CS), and deep learning (DL) are introduced, and their advantages and disadvantages are compared. Presently, SSP inversion relies on sonar observation data, limiting its applicability to areas that can only be reached by underwater observation systems. Furthermore, these methods are unable to predict the distribution of sound velocity in future time. Therefore, the mainstream trend in future research on SSP construction will involve comprehensive utilization of multi-source data to offer elastic sound velocity distribution estimation services for underwater users without the need for sonar observation data. Full article

Conventional matched-field processing (MFP) for acoustic source localization is sensitive to environmental mismatches because it is based on the wave propagation model and environmental information that is uncertain in reality. In this paper, a mode-predictable sparse Bayesian learning (MPR-SBL) method is proposed to increase robustness in the presence of environmental uncertainty. The estimator maximizes the marginalized probability density function (PDF) of the received data at the sensors, utilizing the Bayesian rule and two hyperparameters (the source powers and the noise variance). The replica vectors in the estimator are reconstructed with the predictable modes from the decomposition of the pressure in the representation of the acoustic normal mode. The performance of this approach is evaluated and compared with the Bartlett processor and original sparse Bayesian learning, both in simulation and using the SWellEx-96 Event S5 dataset. The results illustrate that the proposed MPR-SBL method exhibits better performance in the two-source scenario, especially for the weaker source. Full article

Matched field processing (MFP) is an established technique for source localization in known multipath acoustic environments. Unfortunately, in many situations, imperfect knowledge of the actual propagation environment and sidelobes due to modal interference prevent accurate propagation modeling and source localization via MFP. To suppress the sidelobes and improve the method’s robustness, a linear frequency-difference matched field processing (LFDMFP) method for estimating the source range is proposed. A two-neighbor-frequency high-order cross-spectrum between the measurement and the replica of each hydrophone of the vertical line array is first computed. The cost function can then be derived from the dual summation or double integral of the high-order cross-spectrum with respect to the depth of the hydrophones and the candidate sources of the replicas, where the range that corresponds to the minimum is the optimal estimation. Because of the larger modal interference distances, LFDMFP can efficiently provide only one optimal range within the same range search interval rather than some conventional matched field processing. The efficiency of the presented method was verified using simulations and experiments. The LFDMFP unambiguously estimated the source range in two experimental datasets with average relative errors of 2.2 and 1.9%. Full article

Shallow water passive source localization is an essential problem in underwater detection and localization. Traditional matched-field processing (MFP) methods are sensitive to environment mismatches. Many neural network localization methods still have room for improvement in accuracy if they are further adjusted to underwater acoustic characteristics. To address these problems, we propose a deep learning localization method via improved input features and network structure, which can effectively estimate the depth and the closest point of approach (CPA) range of the acoustic source. Firstly, we put forward a feature preprocessing scheme to enhance the localization accuracy and robustness. Secondly, we design a deep learning network structure to improve the localization accuracy further. Finally, we propose a method of visualizing the network to optimize the estimated localization results. Simulations show that the accuracy of the proposed method is better than other compared features and network structures, and the robustness is significantly better than that of the MFP methods. Experimental results further prove the effectiveness of the proposed method. Full article

Underwater Sound Speed Profile (SSP) distribution is crucial for the propagation mode of acoustic signals, so fast and accurate estimation of SSP is of great importance in building underwater observation systems. The state-of-the-art SSP inversion methods include frameworks of matched field processing (MFP), compressive sensing (CS), and feed-forward neural networks (FNNs), among which the FNN shows better real-time performance while maintaining the same level of accuracy. However, the training of FNN needs quite a lot historical SSP samples, which is difficult to satisfy in many ocean areas. This situation is called few-shot learning. To tackle this issue, we propose a multi-task learning (MTL) model with partial parameter sharing among different training tasks. By MTL, common features could be extracted, which accelerates the learning process on given tasks, and reduces the demand for reference samples, enhancing the generalization ability in few-shot learning. To verify the feasibility and effectiveness of MTL, a deep-ocean experiment was held in April 2023 in the South China Sea. Results show that MTL outperforms the other mainstream methods in terms of accuracy for SSP inversion, while inheriting the real-time advantage of FNN during the inversion stage. Full article

In this research, a new application using broadband ship noise as a source-of-opportunity to estimate the scattering field from the underwater targets is reported. For this purpose, a field trial was conducted in collaboration with JASCO Applied Sciences at Duncan’s Cove, Canada in September 2020. A hydrophone array was deployed in the outbound shipping lane at a depth of approximately 71 m to collect broadband noise data from different ship types and effectively localize the underwater targets. In this experiment, a target was installed at a distance (93 m) from the hydrophone array at a depth of 25 m. In this study, a matched field processing (MFP) algorithm is utilized for localization. Different propagation models are presented using Green’s function to generate the replica signal; this includes normal modes in a shallow water waveguide, the Lloyd-mirror pattern for deep water, as well as the image model. We use the MFP algorithm with different types of underwater environment models and a proposed estimator to find the best match between the received signal and the replica signal. Finally, by applying the scatter function on the proposed multi-channel cross correlation coefficient time-frequency localization algorithm, the location of target is detected. Full article

In order to improve the robustness and positioning accuracy of the matched field processing (MFP) in underwater acoustic systems, we propose a conditional probability constraint matched field processing (MFP-CPC) algorithm in this paper, which protects the main-lobe and suppresses the side-lobe to the AMFP by the constraint parameters, such as the posterior probability density of source locations obtained by Bayesian criterion under the assumption of white Gaussian noise. Under such constraint, the proposed MFP-CPC algorithm not only has the same merit of a high resolution as AMFP but also improves the robustness. To evaluate the algorithm, the simulated and experimental data in an uncertain shallow ocean environment is used. From the results, MFP-CPC is robust to the moored source, as well as the moving source. In addition, the localization and tracking performances of using the proposed algorithm are consistent with the trajectory of the moving source. Full article

Terahertz (THz) time of flight (TOF) tomography systems offer a new measurement modality for non-destructive evaluation (NDE) of the subsurface layers of protective coatings and/or laminated composite materials for industrial, security and biomedical applications. However, for thin film samples, the time-of-flight within a layer is less than the duration of the THz pulse and consequently there is insufficient range resolution for NDE of the sample under test. In this paper, matched field processing (MFP) techniques are applied to thickness estimation in THz TOF tomography applications, and these methods are demonstrated by using measured THz spectra to estimate the the thicknesses of a thin air gap and its depth below the surface. MFP methods have been developed over several decades in the underwater acoustics community for model-based inversion of geo-acoustic parameters. It is expected that this research will provide an important link for THz researchers to access and apply the robust methods available in the MFP literature. Full article

Matched field processing (MFP) is an effective method for underwater target imaging and localizing, but its performance is not guaranteed due to the nonuniqueness and instability problems caused by the underdetermined essence of MFP. By exploiting the sparsity of the targets in an imaging area, this paper proposes a compressive sensing MFP (CS-MFP) model from wave propagation theory by using randomly deployed sensors. In addition, the model’s recovery performance is investigated by exploring the lower bounds of the coherence parameter of the CS dictionary. Furthermore, this paper analyzes the robustness of CS-MFP with respect to the displacement of the sensors. Subsequently, a coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP) algorithm is proposed to overcome the high coherent dictionary problem in special cases. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed CS-MFP method. Full article

Owing to the absorbing, refracting and scattering effects of the propagation medium, electromagnetic (EM) energy will degrade with the increment of propagation range, and the maximum value exists at the point of the radiating source. Employing this phenomenon, this paper introduces a novel approach to detect the location of EM transmitters in an atmospheric duct environment. Different from previous matched-field processing (MFP) methods, the proposed method determines the source location through reconstructing the forward propagation field pattern by the backward adjoint integration of the parabolic equation (PE) propagation model. With this method, the repeated computations of PE used in the MFP methods are not needed. The performance of the method is evaluated via numerical simulations, where the influences of the measurement noise and the geometry of the receiver array on the localization results are considered. Full article