2009年03月30日

Journal of Geodesy (2005) 78: 528-534，-0001，（）：

-1年11月30日

One of the typical approaches to linear, inequality-constrained adjustment (LICA) is to solve a least-squares (LS) problem subject to the linear inequality constraints. The main disadvantage of this approach is that the statistical properties of the estimate are not easily determined and thus no general conclusions about the superiority of the estimate can be made. A new approach to solving the LICA problem is proposed. The linear inequality constraints are converted into priorinformation on the parameters with a uniform distribution, and consequently the LICA problem is reformulated into a Bayesian estimation problem. It is shown that the LS estimate of the LICA problem is identical to the Bayesian estimate based on the mode of the posterior distribution. Finally, the Bayesian method is applied to GPS positioning. Results for four field tests show that, when height information is used, the GPS phase ambiguity resolution can be improved significantly and the new approach is feasible.

Linearl inequality-constrained adjustment (， LICA)， -Least-squares estimation-Bayesian estimation-Uniform prior distribution-GPS positioning

2009年03月30日

-1年11月30日

2009年03月30日

-1年11月30日

2009年03月30日

Trans. Nonferrous Met. Socd. China Apr. 2001 Vol.11 No.2，-0001，（）：

-1年11月30日

Based on the assumption that the slope bodies are rigid, the dynamic model of the landsiding (forward mod-el) was put forward. According to the dynamic model, the system equations of Kalman filter were constituted. The me-chanical status of a slope was hence combined with the monitoring data by Kalman filter. The model uncertainties or mod-el errors could also be considered through some fictitious observation equations. Different from existed methods, the pre-sented method can make use for not only the statistic information contained in the data but also the information provided by the mechanical and geological aspect of slopes. At last a numerical example was given out to show the feasibility of the method.

dynamic model， Kalman filter， rigid body， landslide monitoring

2009年03月30日

-1年11月30日

• 朱建军 邀请

中南大学，湖南

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