International Journal of Naval Architecture and Ocean Engineering

The Society of Naval Architects of Korea (대한조선학회)
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Seafloor terrain detection from acoustic images utilizing the fast two-dimensional CMLD-CFAR

  • Wang, Jiaqi (Acoustic Science and Technology Laboratory, Harbin Engineering University) ;
  • Li, Haisen (Acoustic Science and Technology Laboratory, Harbin Engineering University) ;
  • Du, Weidong (Acoustic Science and Technology Laboratory, Harbin Engineering University) ;
  • Xing, Tianyao (Acoustic Science and Technology Laboratory, Harbin Engineering University) ;
  • Zhou, Tian (Acoustic Science and Technology Laboratory, Harbin Engineering University)
  • Received : 2019.11.18
  • Accepted : 2020.11.18
  • Published : 2021.11.30
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Abstract

In order to solve the problem of false terrains caused by environmental interferences and tunneling effect in the conventional multi-beam seafloor terrain detection, this paper proposed a seafloor topography detection method based on fast two-dimensional (2D) Censored Mean Level Detector-statistics Constant False Alarm Rate (CMLD-CFAR) method. The proposed method uses s cross-sliding window. The target occlusion phenomenon that occurs in multi-target environments can be eliminated by censoring some of the large cells of the reference cells, while the remaining reference cells are used to calculate the local threshold. The conventional 2D CMLD-CFAR methods need to estimate the background clutter power level for every pixel, thus increasing the computational burden significantly. In order to overcome this limitation, the proposed method uses a fast algorithm to select the Regions of Interest (ROI) based on a global threshold, while the rest pixels are distinguished as clutter directly. The proposed method is verified by experiments with real multi-beam data. The results show that the proposed method can effectively solve the problem of false terrain in a multi-beam terrain survey and achieve a high detection accuracy.

Keywords

Acknowledgement

The funders are National Key R&D Program of China (2017YFC0306000, 2016YFC1402303), National Natural Science Foundation of China (NSFC) (U1809212, U1709203, 41576102).

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