共查询到17条相似文献，搜索用时 140 毫秒
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基于多波束测深的地形定位是水下潜器导航技术研究和发展的重点,多波束测深数据的高精度快速重采样是水下地形匹配定位的前提。传统的实时抽稀方法因对多波束测深数据模型的过分简化而效果欠佳。参考Douglas-Peucker算法和点云数据抽稀方法,采用角度-弦高联合准则对多波束每ping数据进行抽稀处理,参考导航地形图对抽稀后的多ping数据基于点云离散度进行二次抽稀处理,从而实现多波束测深数据的高精度快速抽稀处理。典型的数学仿真地形和实测多波束条带数据实验表明:文中提出的抽稀方法数据抽稀率仿真地形在85%以上,实测地形在90%以上,数据抽稀前后点云构成的曲面DEM误差在3%以内,并且算法实时性较好。 相似文献
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相干声纳多波束与传统型多波束测深系统综合对比与实验分析 总被引：1，自引：0，他引：1
在工作原理、技术参数、采集软件和处理软件功能与操作,以及数据实测等方面对相干声纳多波束测深系统Geoswath Plus和传统型多波束测深系统Seabeam 1180进行了详细对比。对比表明,两套多波束系统的工作原理完全不同;传统型多波束测深系统在探头下方数据密集,两侧的数据逐渐变稀,相干声纳多波束测深系统在探头正下方的数据比较稀疏,探头两侧的数据较密集;相干声纳多波束测深系统Geoswath Plus的采集和处理软件实现了一体化,具有较直观的数据质量监控功能,传统型多波束测深系统Seabeam 1180可应用的后处理软件较多,功能更丰富;通过实测数据验证,两套多波束测深系统在数据测量精度上具有较好的一致性,不符值数列的标准偏差为3%—4%。对比为多波束测深系统的引进和选型提供了参考依据。 相似文献
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受声线弯曲的影响,多波束测深的边缘波束的数据质量较低,而单波束测深受声线弯曲的影响比较小。结合多波束覆盖面大和声速剖面误差对单波束影响相对较小的特点,研究了多波束和单波束的测深数据融合方法,利用同一位置单波束和多波束测深数据的差值,拟合一个与坐标位置相关的误差模型,并利用该误差曲面对多波束测深数据进行综合改正,从而提高多波束测深的数据质量。 相似文献
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针对当前高密度多波束水深数据抽稀后所构建数字水深模型(digital depth model,DDM)的航海安全性缺少估计这一问题,分别以最浅点法、最近点法和平均值法3种常用方法抽稀水深数据并构建DDM,在此基础上,分析不同抽稀方法所构建DDM随尺度变化的深度保证率变化规律,采用统计分析的方法建立DDM深度保证率与抽稀尺度、海底地形复杂因子之间的数学回归模型。实验表明:该回归模型不仅可用于估算基于不同抽稀方法所构建DDM的深度保证率,也为确定满足适合的DDM深度保证率所需要的抽稀尺度提供了理论依据。 相似文献
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波浪破碎是一个强非线性过程,破碎时产生的大量气泡在海面上表现为白冠,白冠覆盖率是刻画波浪破碎一个重要参数。研究表明,白冠覆盖率与海上风速、海浪状态和大气稳定度等多种海洋环境因素有关。综合前人的观测数据,本文给出了更为可靠的依赖风速的白冠覆盖率公式,发现海水温度越高,白冠覆盖率越大。提出了以波浪破碎耗散函数为参数的白冠覆盖率公式,同时发现波龄小于某个临界值时,白冠覆盖率随波龄增大,波龄大于临界值时,白冠覆盖率保持不变,该临界值随风速增大而减小。 相似文献
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基于随机集合的非传统型有效波极值模型 总被引：1，自引：0，他引：1
The analysis and design of offshore structures necessitates the consideration of wave loads. Realistic modeling of wave loads is particularly important to ensure reliable performance of these structures. Among the available methods for the modeling of the extreme significant wave height on a statistical basis, the peak over threshold method has attracted most attention. This method employs Poisson process to character- ize time-varying properties in the parameters of an extreme value distribution. In this paper, the peak over threshold method is reviewed and extended to account for subjectivity in the modeling. The freedom in selecting the threshold and the time span to separate extremes from the original time series data is incorpo- rated as imprecision in the model. This leads to an extension from random variables to random sets in the probabilistic model for the extreme significant wave height. The extended model is also applied to different periods of the sampled data to evaluate the significance of the climatic conditions on the uncertainties of the parameters. 相似文献
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Automated threshold selection methods for extreme wave analysis 总被引：2，自引：0，他引：2
The study of the extreme values of a variable such as wave height is very important in flood risk assessment and coastal design. Often values above a sufficiently large threshold can be modelled using the Generalized Pareto Distribution, the parameters of which are estimated using maximum likelihood. There are several popular empirical techniques for choosing a suitable threshold, but these require the subjective interpretation of plots by the user.In this paper we present a pragmatic automated, simple and computationally inexpensive threshold selection method based on the distribution of the difference of parameter estimates when the threshold is changed, and apply it to a published rainfall and a new wave height data set. We assess the effect of the uncertainty associated with our threshold selection technique on return level estimation by using the bootstrap procedure. We illustrate the effectiveness of our methodology by a simulation study and compare it with the approach used in the JOINSEA software. In addition, we present an extension that allows the threshold selected to depend on the value of a covariate such as the cosine of wave direction. 相似文献
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基于数据挖掘的GF-1 遥感影像绿潮自适应阈值分区智能检测方法研究 总被引：1，自引：0，他引：1
由于受到云雾的影响，可见光影像能够高效用于绿潮检测的数据源较为有限，特别是云覆盖较为严重的可见光影像，基本无法用于检测绿潮。即使影像数据是在薄云、薄雾、无云覆盖的情况下获取的，由于其光谱反射值存在较大差异，依然很难采用同一阈值进行绿潮检测。基于此，为了提高可见光影像的利用率，实现不同云覆盖情况下，绿潮的高精度自适应阈值的自动检测，本文以GF-1影像为数据源，首先采用K-means聚类和C4.5决策树方法实现影像云覆盖情况的自动识别；其次，选取大量不同云覆盖情况下子图像样本（每个子图像样本中均包含绿潮和海水两类），分析得出不同云覆盖情况下绿潮和海水的区分阈值y与影像光谱差x=bandnir-bandred之间所具有的线性关系；然后，利用分析得出的线性关系提出一种适用于GF-1影像的绿潮分区自适应阈值自动检测方法。最后，为验证提出方法的有效性，分别采用NDVI方法、EVI方法和本文提出的自适应阈值自动检测方法进行绿潮提取实验。实验结果表明，对于GF-1卫星遥感数据，本文提出的绿潮自适应阈值分区自动检测方法明显优于传统的NDVI和EVI检测方法，不仅提高了绿潮的监测精度，而且实现了绿潮提取的全自动化。 相似文献
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The material of marine sediments is commonly derived from multiple sources or processes. Consequently, individual sediment samples can be viewed as mixtures of two or more distinct geochemical subpopulations. Certain quantitative procedures such as threshold value analysis are capable of isolating and identifying the subpopulations that are present within a suite of sediment samples through an analysis of bulk sediment compositional data. Thus, these methods are useful in mineral exploration programs because they facilitate the task of discriminating between samples containing background versus anomalous chemical signals. This study reports the development and testing of computer software (ProbabilityGrapher) for threshold value analysis using a probability graphing technique. This program is designed for microcomputers so that it can be readily applied in field situations where the rapid in situ reduction and interpretation of geochemical data is important to the planning and continuation of an exploration survey. The software can also be used in concert with the Q—LM software package previously developed for Q‐mode factor analysis and linear modeling, thus providing a powerful combination of data reduction and interpretation tools for investigating sediment composition information. 相似文献