Sonar Detection Research Articles

UUVDNet: An efficient unmanned underwater vehicle target detection network for multibeam forward-looking sonar

Precise localization of unmanned underwater vehicle (UUV) on multi-beam forward-looking sonar (MFLS) is a key technology in underwater robotic exploration. However, large appearance change and weak features of targets in MFLS image, pose a serious challenge on target detection. This paper proposes an efficient UUV detection model with an enhanced training scheme and convolutional block attention mechanism. The proposed training scheme is composed of dual-path gradient information optimization and adaptive loss adjustment, which improves the network’s detection capabilities and the underwater environment adaptability. The convolutional block attention module (CBAM) is developed for extracting channel and spatially weighted feature maps, and providing a mechanism to adaptive feature refinement. We construct a comprehensive sonar detection dataset from field experiments. By comparing existing state-of-the-art detection algorithms, our proposed UUVDNet outperforms Faster R-CNN, Cascade R-CNN, CenterNet, SSD, YOLOv8n, DPFIN, and MBSNN in terms of mAP50–95 by 39.5%, 35.7%, 8.4%, 14.0%, 2.5%, 13.6%, and 21.8%, respectively. Furthermore, our inference model size is the most compact among all detection models, with a size of 5.1M. Final ablation experiments effectively showcase the proposed components’ efficiency.

Effective deployment strategies for optimizing area coverage in multistatic sonar detection based on Cassini oval approximation and a virtual force algorithm

Multistatic sonar is regarded as a highly promising method for detecting quiet submarines over long distances. This technique exhibits high positioning accuracy, flexible configurations, easy concealment, and robust resistance to interference. However, the sources are more expensive than receivers and performance of multistatic sonar depends strongly on the relative positions of the sources and receivers. In this work, we study the problem of optimal deployment of receivers for one source and multiple receivers type multistatic sonar to achieve maximum area coverage. The area coverage of multistatic sonar is a multi-objective optimization problem (MOOP) balancing maximum coverage against minimal cost. To simplify the MOOP, the area enclosed by a Cassini oval is approximated to two circles and the normalized area difference is analyzed. Then the required number of receivers within a rectangular area of interest (AOI) is calculated based on full coverage theory. So the MOOP is simplified into a single-objective problem focused on achieving maximal coverage with a specific number of receiver nodes. An enhanced virtual force algorithm named Delaunay triangulation hole repair virtual force algorithm (DTHRVFA) is introduced to optimize the deployment positions of receivers as a heterogeneous nodes deployment problem. Simulation results show significant improvement in coverage compared with various methods of heterogeneous nodes deployment. The proposed method effectively addresses area coverage problems associated with the deployment of one source and multiple receivers type multistatic sonar receivers.

Underwater Image Recognition using Machine Learning

Machine Learning is the branch of Artificial Intelligence in which a computer is fed with data and based on that data it tries to find out solution on its own. It encompasses the procedure for feeding algorithms information to create the algorithms realize patterns in the data and then increase the performance of the algorithms. A Convolutional Neural Network (CNN) is a type of a deep learned an algorithm that has been created for image processing when using convolutional layers to automatically and in a hierarchical way learn features from the input images. Computers can perform well when it comes to image recognition and classification because of its capacity to detect and record such features as edges, or texture, and shapes among others. A rise in focusing on processing underwater images is essential for various research purposes necessary in marine biology, economy as well as in the management of species’ biodiversity. Observance of such organisms as plankton and Posidonia Oceanic allows determining environmental shifts, global warming, and impact of people on sea creatures. These include respectively planktons that are fundamental for oxygen generation, climatic events and the Posidonia Oceanic, which helps improve the sea Biodiversity and water quality. In the organisation study, image processing supplement the physio-chemical analysis and the sonar detection system. The performances of deep learning models, especially the CNNs, in underwater image processing are significantly better than the conventional methodologies. Preprocessing is important because images are often low-quality; data augmentation and transfer learning tackle the problems of a small dataset and class imbalance, which allow you to save computations during training. Through human activities, marine trash remains a menace to deep sea ecosystems and marine organisms calling for proper debris control.

Enhancing underwater target detection: Fusion of spatio‐temporal incompletely‐aligned AIS and sonar information via DTW and multi‐head attention mechanism

AbstractIn the field of underwater target detection, the passive sonar is an important means of long‐distance target detection. The sonar detection information typically includes both surface and underwater targets, whereas it is a great challenge on effectively distinguishing between surface and underwater targets solely based on sonar information. Effective fusion of sonar and AIS (Automatic Identification System) data can leverage their complementary nature to compensate for the limitation of sonar information. However, the sonar information and AIS information are acquired based on different detection principles and systems, which are essentially multi‐source heterogeneous information with obvious spatio‐temporal misalignment in nature. Existing fusion methods normally struggle to effectively align sonar and AIS data in both time and space subject to the complexity of the problem. In this study, the Dynamic Time Warping (DTW) algorithm is applied to align sonar and AIS data in the time domain. In addition, a deep learning algorithm with multi‐head attention mechanism is proposed to achieve the spatial alignment of sonar and AIS data, where the matching between the surface targets in AIS data and the same surface targets in sonar data can also be successfully achieved. It provides a priori knowledge to enhance the underwater target detection of the passive sonar by eliminating the interference of the surface targets. Based on the attention mechanism, the abstract features extracted from the intermediate‐layer of the neural networks are found to be effective to represent the typical features of the target motion trajectories, which also demonstrates the effectiveness of the attention mechanism. The experiment results show that the proposed method can successfully achieve a MatchingSucccessRate of over 95% between the AIS targets and sonar detection targets.

Effects of wall heating on wall pressure fluctuations and flow noise in a low-Reynolds-number turbulent channel flow with temperature-dependent viscosity

Wall pressure fluctuations and flow noise substantially degrade sonar detection performance and the acoustic stealth performance of underwater vehicles. This paper numerically investigates the effects of wall heating on wall pressure fluctuations in turbulent channel flow of water with temperature-dependent viscosity, exploring a novel method for controlling wall pressure fluctuations and flow noise in underwater vehicles. Large-eddy simulation (LES) is employed for the numerical calculation of the flow field, while a hybrid method combining LES with Lighthills acoustic analogy is employed to predict flow noise. The numerical results show that when the temperature difference between the wall and the incoming flow is 30 K and 50 K, the peak root-mean-square pressure fluctuations decrease by 6.76% and 8.91%, respectively. Wall heating stabilizes the pressure field near the wall, with the spectral levels of wall pressure fluctuations showing average decreases of approximately 1 dB and 2 dB. Wall heating weakens the energy-containing structures of wall pressure fluctuations and increases the overall convection velocity by 1.22% and 3.81%, respectively. Flow structure analysis reveals that the weakening of energy-containing structures results from the suppression of the vortex structures in the near-wall region. In the wall heating cases, peak turbulent kinetic energy decreases by 12.6% and 15.8%, respectively. Moreover, the sound pressure level of flow noise decreases with increasing wall temperature, with the maximum noise reduction exceeding 3 dB. Previous studies have not yet explored the effects of viscosity reduction caused by wall heating on wall pressure fluctuations and flow noise. This study demonstrates that wall heating is a promising method for reducing wall pressure fluctuations and flow noise.

The Correlation analysis between ambient noise level at low-frequency range and wind speed in shallow water

The ambient noise level in the ocean is an important consideration in the enhancement of passive sonar detection performance as well as undersea surveillance in a harbor area. The wind affects considerably the ambient noise level in the ocean. For this reason, many researchers have analyzed the wind dependency of ambient noise levels in the ocean in the last few decades. However, this analysis is mainly limited to more than the frequency of 100Hz, because the ambient noise level below 100 Hz has been affected by sea current. In this study, we have investigated ambient noise levels in the frequency range 10 Hz - 100Hz as the wind speed increases in shallow water. A drifting small vessel is used to exclude the effect of fluid noise generated on the surface of a hydrophone due to the sea current. The correlation between the ambient noise level and wind speed in frequency range 10 - 25 Hz showed from 0.77 to 0.95. Whereas, it was from 0.11 to 0.69 in the frequency range 32 -100 Hz. The maximum correlation appeared at 10 and 16 Hz. This was much higher than those measured in the frequency range 1kHz - 25 kHz.

A novel target detection method with dual‐domain multi‐frequency feature in side‐scan sonar images

AbstractSide‐scan sonar (SSS) detection is a key method in underwater environmental security and subsea resource development. However, many detection approaches primarily concentrate on tracking the evolution path of optical image object detection tasks when using acoustic images, resulting in complex structures and limited versatility. To tackle this issue, we introduce a pioneering dual‐domain multi‐frequency network (D2MFNet) meticulously crafted to harness the distinct characteristics of SSS image detection. In D2MFNet, a novel method for optimizing and improving the detection sensitivity in different frequency ranges called multi‐frequency combined attention mechanism (MFCAM) is proposed. This mechanism amplifies the relevance of dual‐domain features across different channels and spaces. Moreover, we introduce a dual‐domain feature pyramid network (D2FPN) significantly augments the depth and breadth of feature information in underwater small datasets. The methods offer plug‐and‐play functionality with substantial performance enhancements. Extensive experiments are conducted to validate the efficacy of the proposed techniques, and the results showcase their state‐of‐the‐art performance. MFCAM improves the mAP by 16.9% in the KLSG dataset and 15.5% in the SCTD dataset. The mAP of D2FPN was improved by 8.4% in the KLSG dataset and by 9.8% in the SCTD dataset. The code and models will be publicly available at https://dagshub.com/estrellaww00/D2MFNet.

Side-Scan Sonar Image Augmentation Method Based on CC-WGAN

The utilization of deep learning algorithms for side-scan sonar target detection is impeded by the restricted quantity and representativeness of side-scan sonar (SSS) samples. To address this issue, this paper proposes a method for image augmentation using a CC-WGAN network. First, the generator incorporates the Convolutional Block Attention Module (CBAM) to enhance the assimilation of global information and local features in the input images. This integration also improves stability and avoids mode collapse problems associated with the original Generative Adversarial Network. Subsequently, the CBAM is incorporated into the discriminator to facilitate a better understanding of the relevance and significance of input data, thereby enhancing the model’s generalization ability. Finally, based on this model, existing few-sample SSS images are augmented, and we utilize the augmented images for discrimination and detection with YOLOv5. The experimental results show that following training with the SSS dataset that is augmented by this network, the accuracy of target detection increased by 7.6%, validating the feasibility of our proposed method. This method presents a novel solution to the problem of low model accuracy in underwater target detection with side-scan sonar due to limited samples.

DNTFE-Net: Distant Neighboring-Temporal Feature Enhancement Network for side scan sonar small object detection

Side-scan sonar small target detection becomes fundamental work for side-scan sonar applications which holds vital importance for marine engineering, maritime military, and so on. However, existing algorithms suffer from performance degradation and poor portability when sonar data is limited. We find that benefit from the strong temporal correlation of sonar images, background information from the long-term images and foreground information from the short-term images can be used to enhance small targets in the current image. To fully utilize these two cues, this paper presents the Distant Neighboring-Temporal Feature Enhancement Network (DNTFE-Net) for detecting small objects in sonar images. The algorithm is based on the signal acquisition process of sonar devices, which is considered a continuous time sequence. Firstly, a flexible foreground feature alignment module is developed to align the foreground information between neighboring signals and the current signal so that foreground information can be used to enhance small target features. Then, we propose a background comparison module for selecting distant-temporal signals that provide the most contextual information in order to improve the ability of migrating to different domains. Finally, a foreground background feature amalgamation module is designed to fuse neighboring and distant-temporal signals for enhancing small target features. Furthermore, real sonar data is collected to compose the real continuous sonar dataset. Our DNTFE-Net exhibits superior recognition performance on the real sonar dataset compared to other popular methods, with an improvement of at least 27%. Our algorithms are publicly available at https://github.com/zbyhnu/DNTFE-Net.git.

Multi-scale fusion and efficient feature extraction for enhanced sonar image object detection

Sonar imaging is an underwater detection technology that relies on the transmission and reception of acoustic pulse waves. This technique plays a crucial role in various domains including underwater archaeology, energy exploration, and oceanographic surveying. A primary challenge in sonar imaging is the low signal-to-noise ratio and significant noise interference, issues that are influenced by the constraints of equipment performance and the underwater environment. Traditional object detection techniques have been inadequate in effectively extracting deep features from sonar images possessing targets with complex structures and have also demonstrated shortcomings in the fusion processing of target features at multiple scales, thereby affecting the robustness and accuracy of object detection. To improve the performance of sonar image object detection, we propose an advanced detection framework that integrates efficient feature extraction with multi-scale feature fusion. We employ EfficientNet as the backbone network. EfficientNet exhibits excellent feature extraction capabilities through comprehensive adjustments of depth, width, and resolution. We introduce a dual-channel attention module that blends Squeeze-and-Excitation (SE) and Efficient Channel Attention (ECA) mechanisms to amplify the expression of crucial feature channels and suppress the lesser ones. Additionally, we utilize a modified bidirectional feature pyramid network (BiFPN) to strengthen the integration of features across different layers. Employing these methods, we amalgamate the features into a shared weights classification network and bounding box prediction network for accurate target class discernment and localization. The experimental outcomes provide evidence of the notable superlative nature of the proposed framework in sonar image object detection, effectively ameliorating detection performance amidst noise interference.

Automatic Identification of Sunken Oil in Homogeneous Information Perturbed Environment through Fusion Image Enhancement with Convolutional Neural Network

With the development of the marine oil industry, leakage accidents are one of the most serious problems threatening maritime and national security. The spilt crude oil can float and sink in the water column, posing a serious long-term threat to the marine environment. High-frequency sonar detection is currently the most efficient method for identifying sunken oil. However, due to the complicated environment of the deep seabed and the interference of the sunken oil signals with homogeneous information, sonar detection data are usually difficult to interpret, resulting in low efficiency and a high failure rate. Previous works have focused on features designed by experts according to the detection environments and the identification of sunken oil targets regardless of the feature extraction step. To automatically identify sunken oil targets without a prior knowledge of the complex seabed conditions during the image acquisition process for sonar detection, a systematic framework is contrived for identifying sunken oil targets that combines image enhancement with a convolutional neural network (CNN) classifier for the final decision on sunken oil targets examined in this work. Case studies are conducted using datasets obtained from a sunken oil release experiment in an outdoor water basin. The experimental results show that (i) the method can effectively distinguish between the sunken oil target, the background, and the interference target; (ii) it achieved an identification accuracy of 83.33%, outperforming feature-based recognition systems, including SVM; and (iii) it provides important information about sunken oil such as the location of the leak, which is useful for decision-making in emergency response to oil spills at sea. This line of research offers a more robust and, above all, more objective option for the difficult task of automatically identifying sunken oils under complex seabed conditions.

A high-resolution DOA estimation via random forest virtual array extension

In sonar detection, underwater recognition, and similar fields, the challenge of using small-scale arrays is frequently encountered. With small-scale arrays, the degrees of freedom (DOFs) and accuracy of direction of arrival (DOA) estimation decrease significantly. Moreover, existing algorithms are less robust in complex environments and typically require substantial computing resources to ensure accurate estimation. To overcome these problems, this paper proposes a high-resolution DOA estimation via Random Forest virtual array extension (RFVAE). The algorithm first trains the random forest (RF) network using real array element data, and thus proposes a virtual covariance reconstruction technique. This technique allows for a substantial increase in the number of virtual array elements. Then, based on the virtual covariance reconstruction technique, a high resolution estimation network technique is proposed, which can increase the accuracy and DOFs of DOA estimation. Experimental results show that the proposed algorithm reduces error by 30% to 80% compared to recent technologies and can provide up to twice as many virtual elements. Additionally, the estimation speed can be increased by 3 to 1000 times, and it has significantly stronger robustness, functioning normally at signal-to-noise ratios 10 dB lower than the conditions under which other algorithms fail.

Ultrathin and pressure-resistant meta-coating with embedded tree-shaped acoustic black hole for broadband low-frequency underwater sound absorption

Acoustic coating is the main technical means to absorb incoming sound from detection sonars. Considering the advance of sonar technology to cope with low frequencies and the underwater working environment, acoustic coatings are required to provide effective broadband low-frequency sound absorption (SA) under hydrostatic pressure. However, current underwater acoustic coating materials have poor low-frequency broadband SA performance, especially under hydrostatic pressure. Herein, we propose an ultrathin meta-coating with tree-shaped acoustic black hole (ABH) units embedded in an elastomer matrix. Each tree-shaped ABH unit consists of multi-scale ABH plates as scatterers, a center-support column as “trunk” and a frame as “skeleton”. This design combines the rich dynamics of multi-scale ABH plates, the pressure-resistance characteristics of the frame and the column, and the damping of the matrix, to collectively lead to superior performance. Capitalizing on the rich local and coupled large-amplitude local resonances of the unit, and strong resistance to static-pressure-induced deformation, high and quasi-perfect SA is achieved in multiple and ultra-wide low-frequency bands at a deep subwavelength scale under different hydrostatic pressures. Notably, quasi-perfect SA (>0.92) is achieved at 500 Hz with a meta-coating whose thickness is 2 % of the sound wavelength. The quasi-perfect SA is also demonstrated experimentally within 1200–7500 Hz, which persists till 1.0 MPa. The SA is still above 0.6 from 3–10 kHz at 4.5 MPa. For SA model under hydrostatic pressures, hyperelastic constitutive model and pressurized dynamic mechanical parameters of the matrix were established. Numerical results are then verified by experimental tests under different hydrostatic pressures. With both high local-modal densities and hydrostatic-pressure resistance, this design represents a new type of acoustic metamaterials and promises broad underwater applications.

Spatiotemporal Analysis of Sonar Detection Range in Luzon Strait

Sonar serves as a critical submarine detection apparatus for naval vessels, with its detection range forming the foundation of its overall performance in underwater surveillance. The Luzon Strait, in the eastern part of the South China Sea, presents a complex hydrographic setting that profoundly influences sonar performance, necessitating mastery of the detection range variation for enhanced anti-submarine operational efficiency. This study employs the Bellhop acoustic propagation model to estimate the transmission loss. Subsequently, a detection probability integration approach is applied to determine the sonar detection range in the Luzon Strait from 2019 to 2023, which is then subjected to statistical analysis. The findings indicate the following. (1) During the summer and autumn, the shallow mixed layer fails to generate a surface duct, resulting in shorter detection ranges that are primarily dependent on the water depth. In the Shallow Water Zone (<150 m), frequent interactions between sound waves and the sea boundaries lead to considerable acoustic energy attenuation, maintaining a short detection range. In the Intermediate Depth Zone (150–2500 m), sound rays retain adequate energy post-seabed reflection, extending the sonar detection to 5–8 km. Beyond 2500 m, the diminishing reflective energy restricts the range to 2–5 km. (2) Conversely, in the winter and spring, the formation of a surface duct becomes the predominant determinant of the detection range, capable of exceeding 10 km, overshadowing the influence of the water depth.

A Method for Multi-AUV Cooperative Area Search in Unknown Environment Based on Reinforcement Learning

As an emerging direction of multi-agent collaborative control technology, multiple autonomous underwater vehicle (multi-AUV) cooperative area search technology has played an important role in civilian fields such as marine resource exploration and development, marine rescue, and marine scientific expeditions, as well as in military fields such as mine countermeasures and military underwater reconnaissance. At present, as we continue to explore the ocean, the environment in which AUVs perform search tasks is mostly unknown, with many uncertainties such as obstacles, which places high demands on the autonomous decision-making capabilities of AUVs. Moreover, considering the limited detection capability of a single AUV in underwater environments, while the area searched by the AUV is constantly expanding, a single AUV cannot obtain global state information in real time and can only make behavioral decisions based on local observation information, which adversely affects the coordination between AUVs and the search efficiency of multi-AUV systems. Therefore, in order to face increasingly challenging search tasks, we adopt multi-agent reinforcement learning (MARL) to study the problem of multi-AUV cooperative area search from the perspective of improving autonomous decision-making capabilities and collaboration between AUVs. First, we modeled the search task as a decentralized partial observation Markov decision process (Dec-POMDP) and established a search information map. Each AUV updates the information map based on sonar detection information and information fusion between AUVs, and makes real-time decisions based on this to better address the problem of insufficient observation information caused by the weak perception ability of AUVs in underwater environments. Secondly, we established a multi-AUV cooperative area search system (MACASS), which employs a search strategy based on multi-agent reinforcement learning. The system combines various AUVs into a unified entity using a distributed control approach. During the execution of search tasks, each AUV can make action decisions based on sonar detection information and information exchange among AUVs in the system, utilizing the MARL-based search strategy. As a result, AUVs possess enhanced autonomy in decision-making, enabling them to better handle challenges such as limited detection capabilities and insufficient observational information.

Shallow sea reverberation suppression based on a range azimuth patch matrix model.

Reverberation is the main background interference for active sonar detection in shallow sea. Reverberation suppression is crucial for enhancing the performance of active sonar. In this paper, a reverberation suppression method based on low-rank sparse decomposition is proposed. First, both the sparseness property of the targets and the non-local self-correlation property of the reverberation are used to construct a range azimuth patch matrix model. The reverberation suppression problem is then transformed into an optimization problem for the recovery of a low-rank sparse matrix. The validity of the proposed method is verified by using the measured data. Results show that, compared with the reverberation pre-whitening and sparse fractional Fourier transform methods, the proposed method significantly improves the reverberation suppression performance and achieves a better detection result when the signal-to-interference ratio is below -2 dB.

Evaluating the Adaptability of Deep Learning-Based Multi-feature Sonar Image Detection Algorithms

Evaluating the Adaptability of Deep Learning-Based Multi-feature Sonar Image Detection Algorithms

Reflection and transmission of ultrasonic waves in a layered structure with a functionally graded porous piezoelectric base

The phenomenon of reflection and transmission of waves offers valuable insights into the internal composition and structural characteristics of materials. This study investigates the reflection and transmission of waves in functionally graded porous piezoelectric materials. These materials, distinguished by their customised electromechanical attributes and gradual property variations, present a promising avenue for optimizing performance across diverse applications, including ultrasonics. The reflection and transmission of ultrasonic waves in a novel structure, consisting of a fluid half-space (FHS) positioned above n porous piezoelectric layers, situated on top of a functionally graded porous piezoelectric half-space (FGPPHS) is studied in this paper. The material properties of FGPPHS are considered to vary along the vertical direction and and resulting equations are solved analytically and numerically. The transfer matrix method is employed to analytically determine the energy ratios and amplitude ratios for reflected and transmitted waves. Numerical computations are performed to study the impacts of frequency, gradation, angle of incidence, and porosity on the energy ratios. Furthermore, the influence of stacking of the number of porous piezoelectric layers above FGPPHS, and the choice of materials (Barium Titanate (BaTiO 3), PZT − 5H, PZT − 7H) in layers and half-space, on the energy ratios are studied. The absolute value of acoustic impedance is plotted for various angles of incidence and porosities. From the graph, it is found that the acoustic impedance can be controlled by adjusting porosity in the structure. This will be helpful in minimizing the energy loss at ceramic-medium interface and improving the mismatch of acoustic impedances at the interfaces of medical ultrasonic imaging devices or underwater sonar detectors, and NDE applications. Further, because of lower acoustic impedance, lower density and stiffness of porous piezoelectric materials, the outcomes of this study will be helpful in designing SAW devices.

Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay

AbstractObjectiveOnce thought to be extirpated from the Chesapeake Bay, fall spawning runs of Atlantic Sturgeon Acipenser oxyrinchus have been rediscovered in the Marshyhope Creek (MC)–Nanticoke River (NR) system of Maryland, United States. High recapture rates in past telemetry surveys suggested a small population in the two connected tributaries. This study aims to generate estimates of abundance and understand within system connectivity for spawning runs in 2020 and 2021.MethodsData from mobile side‐scan sonar surveys and detections of acoustically tagged adults on stationary telemetry receivers were analyzed in an integrated model to estimate spawning season abundance and examine run timing and system connectivity for this population. An array of acoustic receivers was deployed throughout the MC–NR system to monitor the movement of tagged fish during the spawning run period from mid‐August to late October. Side‐scan sonar surveys were conducted weekly in September in an area of high spawner aggregation to generate count data on spawning run abundance.ResultIn 2020 and 2021, 32 (95% credible interval [CRI] = 23–47) and 70 (95% CRI = 49–105) Atlantic Sturgeon, respectively, used the MC–NR system. The lower estimate for 2020 coincided with an earlier end to the spawning run related to cooler September temperatures in that year.ConclusionIn both years, high spawning run connectivity between MC and the upper NR was observed. Overall, run estimates supported previous hypotheses that the MC–NR system supports a very small population and that both MC and the upper NR serve as important areas for spawning activity.

Analysis of Bottom Reverberation Intensity Under Beam-Controlled Emission Conditions in Deep Water

A comprehensive understanding of the characteristics and the formation mechanism of reverberation is the key to improving the performance of the active target detection. In response to the challenge of analyzing the intensity of bottom reverberation in typical deep-sea environments, this study proposes a prediction method for the bottom reverberation intensity under beam-controlled emission conditions. It explains the variation law of bottom reverberation intensity under beam-controlled emission conditions in typical deep-sea environments of the South China Sea through theoretical and simulation analyses. Reverberation intensity of the deep-sea bottom under beam-controlled emission conditions exhibits significant fluctuations during the duration of reverberations in the direct sound zone of the seabed. This phenomenon is closely related to the directionality of the source emission, leading to intermittent reverberation masking and detectable areas in the active sonar detection. In addition, the duration of the high-reverberation zone near the cutoff distance of the direct sound from the seabed is longer under the beam-controlled emission conditions of the emission array located within the surface waveguide layer of the deep sea during winter.