A strip adjustment procedure to mitigate the impact of inaccurate mounting parameters in parallel lidar strips

Lidar (laser scanning) technology has been proven as a prominent technique for the acquisition of high-density and accurate topographic information. Because of systematic errors in the lidar measurements (drifts in the position and orientation information and biases in the mirror angles and ranges) and/or in the parameters relating the system components (mounting parameters), adjacent lidar strips may exhibit discrepancies. Although position and orientation drifts can have a more significant impact, these errors and their impact do not come as a surprise if the quality of the GPS/INS integration process is carefully examined. Therefore, the mounting errors are singled out in this work. The ideal solution for improving the compatibility of neighbouring strips in the presence of errors in the mounting parameters is the implementation of a rigorous calibration procedure. However, such a calibration requires the original observations, which may not be usually available. In this paper, a strip adjustment procedure to improve the compatibility between parallel lidar strips with moderate flight dynamics (for example, acquired by a fixed-wing aircraft) over an area with moderately varying elevation is proposed. The proposed method is similar to the photogrammetric block adjustment of independent models. Instead of point features, planar patches and linear features, which are represented by sets of non-conjugate points, are used for the strip adjustment. The feasibility and the performance of the proposed procedure together with its impact on subsequent activities are illustrated using experimental results from real data.     

基于全站仪工程控制测量的三维间接平差法

目前,利用高精度全站仪三维坐标测量功能建立三维工程控制网已成为一种趋势。针对这一问题的观测数据处理,提出了一种考虑平面观测数据和高程观测数据二者间整体性的三维间接平差方法,详细推导了水平角误差方程,天项距误差方程,斜距误差方程等关键步骤,较之已有的方法,严密性和实用性更强,具有一定的参考价值。     

概括平差模型的解法探讨

分析了概括平差模型系数矩阵可能出现非列满秩情况的原因,研究了如何使系数矩阵为列满秩矩阵,避免了系数矩阵为非列满秩时的复杂解算过程。     

Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.     

Estimation of parameters in a split functional model of geodetic observations (<Emphasis Type="Italic">M</Emphasis><Subscript>split</Subscript> estimation)

The paper presents a method of estimating parameters in two competitive functional models. The models considered here are concerned with the same observation set and are based on the assumption that an observation may result from a realization of either of two different random variables. These variables differ from one another at least in the main characteristic (for example, outliers can be realizations of one variable). A quantity that describes the opportunity of identifying a single observation with one random variable is assumed to be known. That quantity, called the elementary split potential, is strictly referred to the amount of information that an observation can provide about two competitive assumptions concerning the observation distribution. Parameter assessments that maximize the global elementary split potential (concerning all observations), are called M _split estimators. A generalization of M _split estimation presented in the paper refers to the theoretical foundation of M-estimation. An erratum to this article can be found at     

二次平差法在测距仪精度估计中的应用

测距仪标称精度一般是根据基线场上的检测结果确定的,由于仪器检测时的条件与工程现场条件不一致,因而标称精度不能反映观测实际精度,以标称精度定权处理观测数据,影响测量成果的质量;在工程实际中,若多余观测边较多,运用二次平差法进行测距仪精度估计,可正确反映仪器实际观测精度,为仪器性能判别及数据处理提供客观依据。     

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冀鲁豫地区重力场变化与封丘ML4.8地震关系的探讨

利用冀鲁豫测网的流动重力5期观测成果,探讨了重力场变化与封丘地震的关系。研究发现：冀鲁豫交界地区的重力点值在封丘地震前后出现了下降-上升-下降的变化过程,较好地对应了封丘ML4.8地震孕育 发震 震后调整的过程。该重力变化可能是封丘地震的异常反映。     

约束秩亏间接平差模型的虚拟观测算法

约束秩亏间接平差模型的法方程系数矩阵为分块矩阵,其左上角的子块矩阵秩亏,因此无法直接计算分块矩阵的凯利逆矩阵.利用矩阵运算,构建一个能直接求凯利逆的分块矩阵,进而推算出约束秩亏间接平差模型法方程系数矩阵的凯利逆的直接显性表达式.提出将约束条件看作虚拟观测,和原有的秩亏间接平差模型组合成新的误差方程,再和约束条件组成约束...     

冬季南海北部中尺度涡旋的数值研究

南海环流的一个主要特征是上层海洋环流具有多涡结构,海洋中尺度涡旋的演变(时间上的生消和空间上的迁移)是南海环流季节调整的可能方式。文中依据卫星遥感海面高度资料和实际海洋观测所揭示的南海北部存在中尺度涡旋体系的基本事实,采用一个改进了涡分辨(eddy-resolving)普林斯顿海洋模式(POM),对冬季处于强盛的东北季风强迫以及黑潮在巴士海峡入侵的共同作用下的南海北部环流的中尺度涡旋体系进行了数值研究,初步再现了冬季南海北部中尺度涡的生命史。计算结果表明,在实际的气候冬季风应力驱动下,具有的实际侧边界地形的南海北部呈现有强烈的中尺度涡旋。文中探讨了中尺度涡的垂直结构、温盐场的配置以及大尺度水平辐合辐散、海洋垂直运动与之相关的时空结构。由此可以得知,在冬季南海北部中尺度涡旋生命史的不同阶段,上述动力学因子的重要性是相对的。不同的敏感性试验表明,斜压调整是形成冬季南海中尺度涡旋体系的决定性因子;边界的入流和风应力驱动是影响中尺度涡旋运动的主要因素。