经典测量中的点位移-测量之间伪时代的实用方法

R. Duchnowski, P. Wyszkowska
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引用次数: 0

摘要

各种测量技术和数据处理应用于确定大地测量网或建筑物的点位移和变形。考虑到网络变形的经典测量和分析,我们应该认识到测量不是“即时的”。问题出现了:如果一个点(或一些点)在一个历元内的特定测量之间发生位移会发生什么?在这种情况下,观测集将由点位移前后的观测组成,这种假设的观测组可以看作与两个(或多个)伪时代有关。本文的主要目的是研究一些可能处理此类问题的估计方法,即Msplit估计(在两种变体中,平方和绝对Msplit估计)和选择的鲁棒方法,即Huber方法(示例m -估计)和Hodges-Lehmann加权估计(基本r -估计)。第一种方法可以提供网络点坐标的两个(或更多)变体(这里,在点移动之前和之后),在测量期间提供关于网络的两个(或更多)状态的信息。相比之下,鲁棒方法只能减少异常值对计算的网络点坐标的影响。因此,在这种情况下,估计结果将只涉及一个网络状态。实证分析表明,采用Msplit估计可以获得更好、更真实的结果。Huber的方法也可以提供可接受的结果(描述历元开始时的网络状态),只要在点位移之后进行的观测次数不太高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Point displacements during classical measurements – a practical approach to pseudo epochs between measurements
Various measurement techniques and data processing are applied to determine point displacements and deformation of geodetic networks or buildings. Considering classical measurements and analysis of the network deformation, we should realize that the measurements are not “immediate.” The question arises: what happens if a point (or some points) displaces between particular measurements within one epoch. In such a case, the observation set would consist of the observations before and after point displacement, and such hypothetical observation groups can be regarded as related to two (or more) pseudo epochs. The paper's main objective is to examine some estimation methods that would probably deal with such a problem, namely Msplit estimation (in two variants, the squared and the absolute Msplit estimation) and chosen robust methods, namely Huber’s method (example M-estimation) and the Hodges-Lehmann weighted estimation (basic R-estimation). The first approach can provide two (or more) variants of the network point coordinates (here, before and after point movements), providing information about two (or more) states of the network during measurements. In contrast, the robust methods can only decrease the influence of the outliers on the computed network point coordinates. Thus, estimation results would concern only one network state in such a case. The presented empirical analyses show that the better and more realistic results are obtained by applying Msplit estimation. Huber’s method can also provide acceptable results (describing the network state at the epoch beginning) only if the number of observations conducted after the point displacements is not too high.
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