该文基于4-DoF空间至6-Do F空间量值传递数学模型和目标声信号模拟技术，设计并研制了6-Do F水下超短基线定位计量装置。该装置由消声水池、水池4-DoF测控系统、4-DoF扩展安装装置、目标模拟声信标机、水听器、数据控制器，以及标准钢卷尺、万能角度尺、声速剖面仪等标准器具构成，具有6-DoF水下定位计量能力，同时解决了小尺度水池中大定位距离的定位计量问题。经测量不确定度分析评定得出，该装置的相对合成标准不确定度为0.34%，测试试验验证了装置各项功能和工作的稳定性，且装置满足《超短基线水声定位仪（JJG（交通）152-2020）》计量检定要求。该装置在声呐系统计量实验课程建设、实验教学、高校计量认证等方面具有示范辐射作用。

多波束声呐图像是进行海底底质分类的主要数据源之一,由于受海洋噪声、声波散射和混响、仪器设备等因素影响,其经各项常规改正后仍存在明显残差,突出表现在中央波束区和条带重叠区,难以形成高质量的声呐图像。文中分析了多波束声呐图像残差的成因及影响,提出了一种基于多条带最小二乘拟合的多波束声呐图像残差处理方法。首先,得到相邻声脉冲（ping）信号中央区域、重叠区域以及整体趋势的拟合函数;然后,通过拟合函数计算得到中央和重叠区域的残差改正系数;最后,通过改正系数进行残差改正。实验分析表明,该方法在保留原始细节的基础上,有效削弱了残差对声呐图像的影响,对多波束声呐图像处理具有参考和应用价值。

The airborne LiDAR bathymetry (ALB) is one of the most effective technologies to retrieve the topographic and bathymetric maps of coastal zones. The water depth is typically calculated from the sea surface and seafloor peak positions of the ALB waveforms. However, the seafloor topography slope within the footprint scale causes the seafloor waveform stretching and peak shifting when the blue-green laser beam reaches the seafloor, which induces the timing uncertainly to influence the accuracy of the measured seafloor topography. In this work, a correction method in the airborne LiDAR bathymetric error caused by the effect of seafloor topography slope at the footprint scale is proposed to reduce the influence. In this method, the ALB seafloor waveform simulation model is achieved based on the footprint-scale topography parameters model, taking into account the effect of seafloor topography. The ALB error correction equation is obtained based on the quantitative relationship between seafloor topography slope and peak timing of echo waveforms. The developed method is used to correct the ALB data collected near Ganquan Island in the South China Sea and verified by the topography data captured by a ship-borne multi-beam echo sounder. Results show that the mean absolute error and root mean square error are reduced to 9.4 and 12.3 cm after the correction, respectively. Specifically, MAE and RMSE decreased by 35.6% and 33.5%, respectively. © 2022 National Remote Sensing Bulletin. All rights reserved.

为提升声学多普勒流速剖面仪（ADCP）测流精度，促进水文水资源监测水平，基于水体含沙量（0～20）kg/m3的流速水槽，分析了ADCP测流精密度及准确度变化特征。实验表明，ADCP测流精密度随水体含沙量增加而增加，在（0.7～0.9）m/s流速范围内，ADCP的测流精密度受含沙量影响较大，0.3 m/s时则受含沙量影响较小，ADCP的测流准确度随含沙量的增加呈现先增高后降低的变化趋势，在（9～11）kg/m3含沙量范围内测流准确度较高，该范围之外，测流准确度均有不同程度的减小，此外，随着含沙量的增加，其对测流准确度的影响呈减弱趋势。相关分析及结论可为水文观测设备研制及水文科学研究提供参考。

A set of calibration system for multi-beam sonar is designed by the principle and method of underwater acoustic metrology. Using the multi-dimensional operation control device, the calibration for acoustical index is completed in the anechoic pool, including the sound source level and beam width. The calibration for geometrical index is carried out in the large scale and deep-water prototype pool, including the bathymetric accuracy. Components and method of the calibration system are introduced, as well as the expanded uncertainty of the calibration system is presented. By comparing the calibrating value and the indicating or nominal value of the detected multi-beam sonar, test results show that the sound source level error is less than 0.7dB, the beam width indication error is less than 10% and the bathymetry value error is less than 0.2%. © 2021 Institute of Physics Publishing. All rights reserved.

Errors induced by inaccurate or insufficient sound speed profiles (SSPs) in the water column will cause refraction errors in multibeam bathymetries. Most existing methods focus on eliminating errors in shallow water. Thus, to address the problems in a wider range of water depths, a new method is developed. First, beams in each ping are divided into two parts with the nadir beam as a boundary, and beams in each part have one equivalent sound speed profile (ESSP). Then, the overlaps of adjacent swaths are used to estimate the gradients of ESSPs by minimizing the depth difference in the overlaps with the differential evolution (DE) algorithm. To verify the proposed method, two datasets, i.e., one with artificially introduced refraction errors from water depths within 24 m and the other with refraction errors from water depths within 300 m, are calibrated. The results show that the mean and standard deviation (SD) of the depth difference between the results of the proposed method and the in situ measurements in the shallow water data are approximately -0.006 m and 0.006 m, respectively. The mean and SD of the depth difference between the results of the proposed method and the in situ measurements in the deeper water data are approximately less than -0.066 m and 0.266 m, respectively.

Acoustic remote sensing with multibeam echo-sounder systems (MBESs) has been extensively used for coastal and ocean survey works. The imperfect integration of multibeam echo sounder and motion sensor can introduce integration errors, which manifest as high-frequency wobbles in swaths and hinder the accurate expression of high-resolution seabed topographic maps. To address this issue, a precise method is developed to calibrate these integration errors based on a simplified georeferenced model. First, an equivalent attitude coordinate (EAC) system is defined to represent mutual transformation between equivalent attitudes and the beam launch vector; then, with the help of equivalent attitudes, a simplified footprint georeferenced model is deduced that considers the effect of the parameterized integration errors (including time delay, motion scale, yaw misalignment, and lever arm errors); finally, in a selected flat region, integration errors are inverted by regressing the bathymetric data to the corresponding fitted plane via the differential evolution (DE) algorithm. The results indicate that the proposed method can effectively eliminate wobbles in multibeam bathymetries caused by single or multiple integration errors in both shallow- and deep-water areas. By comparing the data before and after calibration with the in situ measurements, the accuracy of the calibrated shallow- and deep-water data is controlled within approximately 0.15% and 0.06% of the water depth, respectively.

高精度水体含沙量测量仪是进行水体研究的重要设备。目前，我国高精度含沙量测量仪大量依赖进口，且国内外尚没有完善的高精度含沙量设备计量检测方法，难以对含沙量测量设备进行规范有效的检定及校准。因此，高精度含沙量设备计量检测方法及溯源技术的研究既可以填补国内含沙量测量仪计量领域的空白，同时也会带来巨大的社会效益和经济效益。本课题通过配置高精度水体悬沙场，利用悬沙场作为标准器计量含沙量的测量仪，建立稳定悬移质泥沙浓度场的数学模型，提出了含沙量测量仪计量校准方法，并研制了格栅式和机械搅拌式含沙量测量仪计量标准装置，提高了含沙量测量仪计量校准能力。实验表明，在含沙量浓度0.1 千克每立方米到30千克每立方米范围之内，当前含沙量测量仪计量标准装置样机所构建悬沙场的相对标准不确定度小于1%,能够满足大多数含沙量测量仪的计量需求。High-precision sand content measuring instrument is an important equipment for water body research.At present,my country's high-precision sand content measuring instrument relies heavily on imports,and there is no comprehensive high-precision sand content measurement method at home and abroad,so it is difficult to carry out standardized and effective verification and calibration for sand content measurement equipment.Therefore,the research of high-precision sand content equipment measurement,detection methods and traceability technology can not only fill the gap in the field of domestic sand content measurement instruments,but also bring huge social and economic benefits.In this project,by configuring a high-precision water body suspended sand field,the suspended sand field is used as a standard instrument to measure the sediment concentration.A mathematical model of the stable suspended sediment concentration field is established,and the test and calibration method of the sediment concentration measuring instrument is proposed.The grid type and mechanical stirring type sand content measuring instrument measurement standard devices are developed,which improve the measurement and calibration ability of the sand content measuring instrument.Experiments have shown that within the range of 0.1 kg/m3 to 30 kg/m3 of sand content,the relative standard uncertainty of the suspended sand field constructed by the current sand content meter measurement standard device prototype is less than 1%,which can meet measurement needs of most of the sand content measurement instrument.

The ability to accurately map the seabed sediments plays an important role in seabed habitat development and stakeholder decision-making. In conventional seabed sediment classification methods, maps of seabed sediment are provided in categorical form (sediment classes). Therefore, the prediction of the sediment compositions in multibeam observational units has become a difficult issue in using conventional methods. To tackle this challenge, a new strategy is developed to realize the subpixel decomposition of seabed sediments. A key attribute of the proposed sediment decomposition model is that it utilizes spatial-spectral information provided by multibeam backscatter angular responses (ARs). First, an AR feature extraction method utilizing a bidirectional sliding window is proposed and a K-means clustering algorithm is used for segmentation. Second, a deep-sea sediment decomposition model based on the fuzzy method is constructed by selecting experimental samples that are distributed within a single clustering region. This model inverts the abundance of each sediment composition in the form of membership degrees. Finally, deep-sea multibeam survey data collected from the central Philippine Sea are used for verification. The overall mean square error and coefficient of determination reach 0.043 and 0.856, respectively. The experimental results show that the new method can accurately decompose deep-sea sediment compositions, thus providing a new technique for deep-sea acoustic sediment remote sensing and quantitative analysis.

Laser point cloud registration is a key step in multisource laser scanning data fusion and application. Aimed at the problems of fewer overlapping regional features and the influence of building eaves on registration accuracy, a hierarchical registration algorithm of laser point clouds that considers building eave attributes is proposed in this paper. After extracting the building feature points of airborne and vehicle-borne light detection and ranging data, the similarity measurement model is constructed to carry out coarse registration based on pseudo-conjugate points. To obtain the feature points of the potential eaves (FPPE), the building contour lines of the vehicle-borne data are extended using the direction prediction algorithm. The FPPE data are regarded as the search set, in which the iterative closest point (ICP) algorithm is employed to match the true conjugate points between the airborne laser scanning data and vehicle-borne laser scanning data. The ICP algorithm is used again to complete the fine registration. To evaluate the registration performance, the developed method was applied to the data processing near Shandong University of Science and Technology, Qingdao, China. The experimental results showed that the FPPE dataset can effectively address the coarse registration accuracy effects on the convergence of the iterative ICP. Before considering eave attributes, the mean registration errors (MREs) of the proposed method in the xoz plane, yoz plane, and xoy plane are 0.318, 0.96, and 0.786 m, respectively. After considering eave attributes, the MREs decrease to 0.129, 0.187, and 0.169 m, respectively. The developed method can effectively improve the registration accuracy of the laser point clouds, which not only solves the problem of matching true conjugate points under the effects of the eaves but also avoids converging to a local minimum due to ICP's poor coarse registration. (C) 2021 Optical Society of America