A Multi-Beam Seafloor Constant False Alarm Detection Method Based on Weighted Element Averaging

Wang, Jian; Li, Haisen; Huo, Guanying; Li, Chao; Wei, Yuhang

doi:10.3390/jmse11030513

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…For ease of visual representation, this research has also established a simulated seabed terrain for actual computations. This terrain sets up the scenario with the multibeam transducer's opening angle at 120°, a slope of 1.5°, and a specific case of the seawater depth at the sea area's central point being 120 meters, calculating the coverage width of each survey line [3].…”

Section: Research Objectivesmentioning

confidence: 99%

Research on the Multibeam Bathymetry Problem in Uneven Seabeds Based on Computational Geometry

Li,

He,

Liu

2024

HSET

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The principle of the multibeam seabed bathymetry system involves emitting dozens to hundreds of beams at once in a plane perpendicular to the course, and then receiving the acoustic waves returned by the seabed through the receiving transducer. This paper will study through computational geometry how to calculate information such as seabed depth, survey line coverage width, and the overlap rate between survey lines based on the received information in uneven seabed areas. This study first discusses the special case where the survey line is parallel to the contour line, defining coverage width and overlap rate. It then uses the sine theorem and triangle solving to calculate physical quantities such as seawater depth, coverage width, and overlap rate with the previous survey line when the seabed has a slope. Sensitivity analysis is conducted from three aspects: depth, slope, and opening angle. This research continues to discuss the general case where the survey line is not parallel to the contour line. By first calculating the angle between the intersection line generated by the seabed section detected by the vessel and the seabed slope surface and the horizontal plane, the problem is transformed into the model under the parallel situation to continue the calculation and solve the coverage width. The study of multibeam bathymetry in uneven seabeds using computational geometry enables the application of multibeam bathymetry in uneven seabed areas, providing a simple calculation method for the field of bathymetry in uneven seabeds and expanding the application conditions of multibeam bathymetry.

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Section: Research Objectivesmentioning

confidence: 99%

Research on the Multibeam Bathymetry Problem in Uneven Seabeds Based on Computational Geometry

Li,

He,

Liu

2024

HSET

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“…This method improves the robustness of fluctuating target detection in heavy-tailed clutter. In [15], they suggested the use of a multibeam seafloor constant false alarm detection method based on weighted element averaging, which is specifically tailored for seafloor applications, improving accuracy in underwater scenarios. Despite these efforts to propose new techniques, achieving satisfactory detection performance has proven to be challenging and complex to implement.…”

Section: Introductionmentioning

confidence: 99%

Clutter Map Constant False Alarm Rate Mixed with the Gabor Transform for Target Detection via Monte Carlo Simulation

Mbouombouo Mboungam,

Zhi,

Fonzeu Monguen

2024

Applied Sciences

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Radar detection is a technology frequently used to detect objects and measure the range, angle, or velocity of those objects. Several studies have been performed to improve the accuracy and performance of detection methods, but they encountered a strong challenge, which was the minimization of false alarms and the distinguishing of real targets from false alarms, especially in nonhomogeneous environments. We propose a new detection method that uses time-frequency analysis tools to improve detection performance and maintain a low constant false alarm rate. Different from existing works, this paper combines the clutter map constant false alarm rate technique with the Gabor transform for accurate target detection in cluttered environments. We suggest the combination of a CFAR detector with a time-frequency method that enables us to tackle challenging scenarios involving near targets. The proposed method allows for locating the exact position of the target by reducing the impact of clutter and maintaining a low rate of false alarms, while the Gabor transform facilitates the extraction of pertinent target characteristics and improves differentiation from clutter. Through experiments and simulations in different scenarios and clutter models, we demonstrate that the method is efficient in measurements and performs well in cluttered environments. This research has a major impact on signal processing and significantly improves target detection in cluttered environments, allowing this method to be deeply developed and implemented.

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“…As the bottom is one of the most important targets, CFAR methods have been used in bottom detection of multibeam echosounders with background noise [18].…”

Section: Introductionmentioning

confidence: 99%

Anti-Interference Bottom Detection Method of Multibeam Echosounders Based on Deep Learning Models

Meng,

Yan,

Zhang

2024

Remote Sensing

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Multibeam echosounders, as the most commonly used bathymetric equipment, have been widely applied in acquiring seabed topography and underwater sonar images. However, when interference occurs in the water column, traditional bottom detection methods may fail, resulting in discontinuities in the bathymetry and distortion in the sonar images. To solve this problem, we propose an anti-interference bottom detection method based on deep learning models. First, the variation differences of backscatter strengths at different incidence angles and the failure conditions of traditional methods were analyzed. Second, the details of our deep learning models are explained. And these models were trained using samples in the specular reflection, scatter reflection, and high-incidence angle regions, respectively. Third, the bottom detection procedures of the along-track and across-track water column data using the trained models are provided. In the experiments, multibeam data with strong interferences in the water column were selected. The bottom detection results of the along-track water column data at incidence angles of 0°, 35°, and 60° and the across-track ping data validated the effectiveness of our method. By comparison, our method acquired the correct bottom position when the traditional methods had inaccurate or even no detection results. Our method can be used to supplement existing methods and effectively improve bathymetry robustness under interference conditions.

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A Multi-Beam Seafloor Constant False Alarm Detection Method Based on Weighted Element Averaging

Cited by 7 publications

References 12 publications

Research on the Multibeam Bathymetry Problem in Uneven Seabeds Based on Computational Geometry

Research on the Multibeam Bathymetry Problem in Uneven Seabeds Based on Computational Geometry

Clutter Map Constant False Alarm Rate Mixed with the Gabor Transform for Target Detection via Monte Carlo Simulation

Anti-Interference Bottom Detection Method of Multibeam Echosounders Based on Deep Learning Models

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