Multibeam amplitude-phase joint adaptive detection and filtering for underwater terrains

Jiawei GAO; Yufei ZHAI; Jianjun ZHU; Baowei CHEN; Tian ZHOU; Weidong DU

doi:10.11990/jheu.202506007

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Multibeam amplitude-phase joint adaptive detection and filtering for underwater terrains

Research Article | 更新时间：2026-03-31

- Multibeam amplitude-phase joint adaptive detection and filtering for underwater terrains
- Journal of Harbin Engineering University Vol. 46, Issue 8, Pages: 1566-1573(2025)
- 作者机构：
  
  1.哈尔滨工程大学水声技术全国重点实验室，黑龙江哈尔滨 150001
  2.海洋信息获取与安全工信部重点实验室(哈尔滨工程大学) 工业和信息化部，黑龙江哈尔滨 150001
  3.哈尔滨工程大学水声工程学院，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
- DOI：10.11990/jheu.202506007
  CLC： TB566
- Received：05 June 2025，
  
  Online First：24 June 2025，
  
  Published：05 August 2025
- 稿件说明：
移动端阅览
Jiawei GAO, Yufei ZHAI, Jianjun ZHU, et al. Multibeam amplitude-phase joint adaptive detection and filtering for underwater terrains[J]. Journal of Harbin Engineering University, 2025, 46(8): 1566-1573.
DOI：

Jiawei GAO, Yufei ZHAI, Jianjun ZHU, et al. Multibeam amplitude-phase joint adaptive detection and filtering for underwater terrains[J]. Journal of Harbin Engineering University, 2025, 46(8): 1566-1573. DOI： 10.11990/jheu.202506007.

摘要

现代多波束声呐在海洋测绘中具有重要意义，然而传统幅相联合检测算法在复杂水域存在角度分界值难以精准确定、测量易含异常值的问题。为有效避免以上问题对水下地形检测质量所造的影响，本文在质量因子模型的基础上，构建角度判决因子理论模型，实现幅相联合检测算法角度分界值的自适应确定，同时构建二次检测与滤波模型对异常值进行实时处理。通过模拟不同坡度地形数据进行仿真试验，验证了角度判决因子理论模型以及异常值二次检测与滤波模型的有效性。湖试结果也进一步表明：在实际环境中角度判决因子模型依然可有效适应不同坡度的地形，实现不同算法间角度分界值的自适应确定，并且二次检测与滤波模型对异常值的检测与滤波依旧高效稳定，与传统坡度滤波算法对比显示，所建立的二次检测模型得出的结果更为平滑，野值点相对更少。本文算法在提升数据质量和减少误差上具有优越性，为获取可靠的地形探测结果提供有效保障。

Abstract

Modern multibeam sonars are of immense significance in marine mapping. However

conventional amplitude-phase joint detection algorithms face challenges in complex waters

such as issues with the accurate determination of angle thresholds and the influence of measurement outliers. To address these issues affecting the quality of detection of underwater terrains

this study constructs an angle decision factor model based on a quality factor model to realize adaptive determination of angle thresholds for the joint detection algorithm. Additionally

a secondary detection and filtering model was developed for real-time outlier processing. Simulation experiments with terrain data of varying slopes were conducted to verify the effectiveness of the angle decision factor model and the outlier secondary detection and filtering model. The Lake test results further reveal that the angle decision factor model can effectively adapt to terrains of different slopes in real environments

thereby achieving adaptive threshold determination. It was also proved that the secondary detection and filtering model remains efficient and stable while performing outlier detection and filtering processing. Compared with conventional slope filtering algorithms

the proposed model yields smoother results with fewer outliers

demonstrating its superiority in improving data quality and minimizing errors for reliable terrain detection. The results of this study can provide effective guidance for obtaining reliable outcomes in the field of terrain detection.

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references

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