Fast Double Selectivity Index-CFAR Detection Method for the Multi-Beam Echo Sounder

Wang, Jiaqi; Li, Haisen; Ma, Jingxin; Chen, Baowei; Xing, Tianyao; Hui, Zhao

doi:10.1080/01490419.2019.1692265

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…The differentiation of false-bottom types allows for corresponding prevention and elimination plans to be established for each type, surpassing the limitations of traditional research. Nonetheless, research on false-bottom imaging algorithms still lags behind other research [41][42][43][44].…”

Section: Discussionmentioning

confidence: 99%

Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data

Xiong,

He,

Fan

et al. 2024

Applied Sciences

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This article presents a summary of three common false-bottom occurrences in echosounder imaging based on an analysis of echosounder data. Utilizing the imaging principle of the echosounder, a comprehensive analysis was conducted and an explanation of each situation’s causes, imaging characteristics, impacts, and solutions is presented. Additionally, the article includes calculations to determine the precise location of the false bottom, which were subsequently validated through actual data collection. To address the two most impactful false-bottom scenarios in target detection, solutions are proposed from two perspectives. By accurately judging the position and imaging characteristics of these false bottoms, the article concludes with an analysis of the causes of false bottoms and presents corresponding solutions. The article aims to facilitate the rapid identification and elimination of false bottoms, thus mitigating their adverse effects on target detection.

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Section: Discussionmentioning

confidence: 99%

Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data

Xiong,

He,

Fan

et al. 2024

Applied Sciences

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“…While processing WCD to the full extent of the swath is a relatively new field due to the inherent difficulties of sidelobe interference, there has recently been increased effort to effectively filter or suppress this interference for various water column targets (de Moustier, 2013;Liu et al, 2019;Schimel et al, 2020;Wang et al, 2020). However, no studies have quantifiably determined the maximum extent to which data can be utilized in the MSR B region.…”

Section: Discussionmentioning

confidence: 99%

“…de Moustier (2013) proposed a Constant False Alarm Rate (CFAR) method to normalize the signals along each time slice and only retain samples that fall within the 75th quartile. Wang et al (2020) also proposed a CFAR-based algorithm (Double Selectivity Index (DSI)-CFAR), which uses a sliding window to determine an adaptive threshold for identifying seafloor and water column targets. Weber (2021) proposed a CFAR detector coupled with a morphology-based classifier to isolate gas seep targets.…”

Section: Introductionmentioning

confidence: 99%

Extended Detection of Shallow Water Gas Seeps From Multibeam Echosounder Water Column Data

Nau

Scoulding

Kloser

et al. 2022

Front. Remote Sens.

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Multibeam echosounder water column data provides a three-dimensional image of features between the water surface and the seafloor. Although this swath of acoustic data can be collected over a wide range of angles, most of the data, at least beyond the range to the first seafloor return, is contaminated by noise created by receiver array sidelobe interference. As a result, the water column data beyond the minimum slant range commonly is excluded from analysis. This paper demonstrates a method to consistently filter and extract targets comprising a gas seep feature across the multibeam swath, including targets within the areas dominated by receiver array sidelobe interference. For each sample range, data are filtered based on the mean plus a certain number (k) of standard deviations of the sample values along that range. The filtering is coupled with a morphological classification to retain only targets of interest while excluding background data and noise. Data were collected over a shallow water artificial gas seep using two different flow rates and at three different vessel speeds. Using the proposed method, 119 of 124 test seeps were identified correctly. Seep targets were identified at all angles across the water column fan up to beam pointing angles of 55°, with 19 of 23 seeps being correctly identified at angles greater than 50°. This method demonstrates that features can be extracted and geolocated in the sidelobe noise when the interference is appropriately filtered. These results will improve the areal extent of multibeam surveys and increase the utility of acoustic data in capturing information on water column targets directly above the seafloor.

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“…It had been validated for performance in shallow water tests. Wang et al proposed a fast 2D double-selective exponential constant false alarm algorithm based on multi-beam sonar [105]. It improves the detection performance of seafloor terrain targets by selecting specific strategies in different directions.…”

Section: Methodmentioning

confidence: 99%

Sonar image intelligent processing in seabed pipeline detection: review and application

Shi,

Cao,

et al. 2024

Meas. Sci. Technol.

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Subsea pipelines rely primarily on imaging sonar for detection and identification. We analyze the imaging principles of side scan sonar, multi-beam sonar, synthetic aperture sonar, seafloor penetrating sonar and forward-looking sonar. We discuss their effectiveness in detecting seabed pipelines, as well as their limitations in image recognition capabilities. As intelligent algorithms have become increasingly important in the field of image processing, we review the sonar image intelligent detection and recognition algorithms in the past six years and summarize the internal principles and application effects of classic algorithms such as SIFT (Scale-Invariant Feature Transform), KMA (K-means algorithm), and CFAR (Constant False-Alarm Rate) that currently show good application prospects. Simultaneously, we review the particular strengths exhibited by these algorithms, such as contour feature extraction, image segmentation and clustering, target recognition under background noise, etc. The research on intelligent processing of sonar images opens up a new way to solve the difficult problem of the seabed targets detection and recognition.

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Fast Double Selectivity Index-CFAR Detection Method for the Multi-Beam Echo Sounder

Cited by 4 publications

References 27 publications

Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data

Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data

Extended Detection of Shallow Water Gas Seeps From Multibeam Echosounder Water Column Data

Sonar image intelligent processing in seabed pipeline detection: review and application

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