基于MBES历史测深结果的深度门跟踪方法

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Signal Processing Pub Date : 2024-11-17 DOI:10.1016/j.sigpro.2024.109808
Tian Zhou Member, IEEE , Weijia Yuan , Maria S. Greco Fellow, IEEE , Fulvio Gini Fellow, IEEE
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引用次数: 0

摘要

门跟踪技术在多波束回波探测的自主探测中起着至关重要的作用。跟踪算法的质量直接影响估计数据的质量。本文基于历史探测结果与当前ping结果几何相似的假设,提出了一种多波束回波探测中深度门跟踪的新方法。考虑豪斯多夫距离度量度量空间的两个子集彼此之间的距离。本文提出了一种基于相似距离概念的评价方法,该方法依赖于修正Hausdorff距离来度量不同ping检测结果之间的相似度,从而确定可以跟踪的历史ping的最大数量。然后,将该模型与迭代最近点配准算法相结合,将历史ping的测深结果与当前ping的测深结果对齐,从而消除了预先配准的需要。利用注册后的数据初始化跟踪粒子滤波器中的粒子分布,并建立每个光束的距离加权重要性函数。实测数据验证表明,该方法能够有效地跟踪海底地形,并提供稳定的栅极跟踪结果。
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Depth gate tracking method based on historical sounding results in MBES
Gate tracking technology plays a crucial role in the autonomous detection of multi-beam echo sounding. The quality of the tracking algorithm directly impacts the quality of the estimate data. Here, we propose a new method for depth gate tracking in multi-beam echo sounding based on the assumption that historical sounding results are geometrically similar to the current ping results. Considering Hausdorff distance measures how far two subsets of a metric space are from each other. An evaluation method based on the concept of similarity distance that relies on the Modified Hausdorff distance is proposed here to measure the similarity between the detection results of different pings and determine the maximum number of historical pings that can be tracked. Then, this model is combined with the iterative closest point registration algorithm to align the bathymetric results of historical pings to the current ping, eliminating the need for prior registration. The registered data are used to initialize the particle distribution in the tracking particle filter and a distance-weighted importance function is established for each beam. Validation on measured data has shown that the proposed method is effective in tracking seabed topography and providing stable gate tracking results.
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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