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     

基于全站仪观测数据的三维平差模型及数据处理

建立基于全站仪多维观测数据的三维网平差模型,用VisualBasic语言编制计算程序,使其自动完成控制网的平差,解决实际应用中全站仪采集数据和成果处理中存在的问题,指导测绘生产,使全站仪更方便地用于地形控制测量、工程控制测量等测绘工程中。     

网形不一致的工程控制网稳定性分析

对于施工周期较长的大型工程,通常需要对控制网进行定期复测和稳定性分析。控制网复测期间,有时会遇到点位破坏或增设新点等情况,从而使两期控制网的网形不一致。针对控制网无固定起算点和网形改变的问题,讨论控制网的稳定性分析方法,并进行实例分析,为控制网的正确使用提供重要保证。     

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。     

导线网坐标差观测值相关平差方法研究

提出了利用普通间接平差和相关间接平差两种平差方法相结合,进行以坐标差作为观测值进行导线网相关平差的方法.在该平差方法中,利用普通的简介平差原理推求出坐标差观测值的相关协因数阵,并以此作为已知数据进行以坐标差观测值作为观测值的导线网的相关平差.     

DZJ2激光垂准仪使用方法及指标的校正

介绍了DZJ2激光垂准仪的性能及基本指标的校正方法,为用户在使用DZJ2激光垂准仪过程中几种基本指标参数的校正提供参考.     

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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...