广义岭估计在解算单线阵CCD卫星影像外方位元素中的应用

针对单线阵CCD卫星影像外方位元素解算过程中存在的问题，在分析以往解决办法的基础上，提出了利用广义岭估计进行解算，并说明了确定广义岭估计参数的方法。实验证明，该方法简便、稳定、有效。     

肇庆地磁台天文方位角测量及精度估计

介绍了肇庆地磁台天文方位角的测量方法、方案及测量结果。同时，做了测量精度的估计，测量精度完全符合《地震及前兆数字观测技术规范》的要求。     

多模型算法在协同定位中的应用

对编队协同中的导航信息融合问题进行了研究。在利用一个定位圆和状态预测相结合的基础上 ,引入多模型算法进行伪测量确定和状态更新 ,提出了基于多模型的协同定位算法。仿真结果表明 ,该算法具有不需要进行定位圆推同的优点 ,并且性能有较大的提高     

主成分与主成分估计

在测量数据处理中,当设计阵存在复共线时,最小二乘估计的性质将变得不好,本文应用主成分估计来改进最小二乘估计。一方面是因为主成分估计具有一些好的性质,另一方面与岭估计和广义岭估计相比,主成分估计的偏参数选取要简便得多,从而大大提高了实用价值。     

GPS水准计算的非参数回归法

非参数回归是一种广义的回归方法,它具有直观,对模型要求不严密,计算简单的优点。本文介绍非参数回归的原理及其左GPS水准计算中的应用,通过对两个测区的试算,得出了一些有益的结论。     

A Bayesian/maximum-entropy view to the spatial estimation problem

The purpose of this paper is to stress the importance of a Bayesian/maximum-entropy view toward the spatial estimation problem. According to this view, the estimation equations emerge through a process that balances two requirements: High prior information about the spatial variability and high posterior probability about the estimated map. The first requirement uses a variety of sources of prior information and involves the maximization of an entropy function. The second requirement leads to the maximization of a so-called Bayes function. Certain fundamental results and attractive features of the proposed approach in the context of the random field theory are discussed, and a systematic spatial estimation scheme is presented. The latter satisfies a variety of useful properties beyond those implied by the traditional stochastic estimation methods.     

三维网平差的方差分量估计

本文将方差分量估计理论运用于三维网平差中,推导出了含定向角未知数及消去定向角未知数的方差分量估计公式,并进行了模拟和实例计算,从而建立了合理的三维网平差随机模型。     

灰色系统GM(n,m)模型参数的递推估计和时变估计

本文给出灰色系统GM(n,m)模型参数的递推最小二乘估计和时变参数估计,前者的优点是可以避免反复求逆矩阵,因而使计算简便,同时克服了文献1中给出的估计方法当观察数据多、矩阵线数高、分配单元增加时,微型机容量不够的弱点,而且随着新数据的到来,通过递推算法自动对系统参数加以修正,提高了模型的自适应性;时变参数估计可以缩小系统的时变性与模型参数非时变性所产生的差异。     

工程岩体强度的估算方法

岩体是由地质过程中形成的岩块和结构面网络组成。由于试件制备、加荷等均极困难,且昂贵而费时,难于像岩块那样进行大量试验来确定岩体之强度。所以,如何通过室内小试件试验来估算大岩体的强度成为工程地质工作者所关注的问题。本文介绍了两种用小试件资料估算大岩体强度的方法及计算实例。     

Evaluating Proportions of Undetected Geological Events in the Case of Erroneous Identifications

Some geological events occur infrequently but still have a significant impact upon reservoir characteristics. By their very nature, however, it can be difficult to properly estimate the proportions of uncommon events because they may not appear during limited sampling. For example, even with 40 observations and an event proportion of 0.05, there is a 0.13 chance that no events will be observed. We provide some results and guidance concerning methods to estimate proportions when such events are not detected. Two cases are discussed, estimating proportions without errors in identification and estimating proportions when errors may arise. It is well-known that the distribution of possible proportions in the error-free case can be calculated using Bayesian analysis. If one assumes a standard uniform distribution as the prior for the proportion, Bayesian analysis gives a Beta distribution for the posterior. The situation becomes more complicated, however, when detection errors are included; the true proportion has a distribution consisting of several Beta distributions. The difference in results between the error-free and with-error situations can be considerable. For example, when 10 error-free observations are made and no uncommon events are detected, there is a 0.50 chance that the true proportion exceeds 0.06 and a 0.10 chance that it exceeds 0.19. Including the effects of erroneous identifications, however, increases the median proportion to 0.09 and the upper decile to 0.27. We also examine the case where there may be prior geological information, which can be incorporated by amending the prior distribution of the proportion. We find that the use of such a prior makes little difference unless there are very few observations or there are major differences between the anticipated and the observed proportions.