期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B1
页码：178-183
出版社：Copernicus Publications
摘要：Modern photogrammetry and, more generally, the current technology for Earth observation are dependent on various forms of data processing. After the sensing or acquisition step, the data are available in digital format and all what has to be done is to calibrate, to orient and to extract georeferenced information. In this context, data processing for trajectory determination, sensor calibration and sensor orientation follows various patterns, all of them particular cases of the general time dependent parameter estimation problem defined by the equation f (t, (t) + v(t), x(t), ˙ x(t)) = 0, where f is the mathematical functional model, t is the time, (t) is the time dependent observation vector, v(t) is a white-noise generalized process vector, x(t) is the parameter vector and ˙ x(t) the time derivative of x(t). A number of different approaches to estimate parameters x(t) from data (t) has been developed according to the particular form of the above model equation. + v = f (x), f ( + v, x) = 0, f (t, (t) + v(t), x(t)) = 0 and ˙ x(t) = f (t, (t) + v (t), x(t)) are examples of model equations leading to network and Kalman filter/smoother solution strategies. Although these two procedures have proven to be well suited to their respective model equation structure, the paper discusses some of their limitations and alternatives, particularly for time dependent problems. The proposed family of methods uses numerical techniques that integrate the rigorous least-squares method and the finite difference methods for the solution of the Boundary-Value problem of Ordinary Differential Equations. Although we do not claim that this has to substitute existing, proven techniques, the paper indicates how hybrid static and dynamic data processing can be easily integrated with this new approach
