Applied geodesy: global positioning system : networks, by Stuart Turner

By Stuart Turner

1987 Springer. Lecture Notes in Earth Sciences 12. Lg.8vo., 393pp., wraps. the rest decrease on decrease fore-edge, VG. mild conceal put on; no proprietor marks.

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Extra resources for Applied geodesy: global positioning system : networks, particle accelerators, mathematical geodesy

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The subsequent discussion is divided into three sections. ments for geodetic applications Orbital precision require- are discussed in Section 2. Then the necessary theory is developed in Section 3. In Section 4 we present applications. 2. PRECISION REQUIREMENTS The accuracy of orbits needed to obtain baseline estimates depends mainly on the length of the baseline (see Ref. 4, Eq. r p of a certain accuracy (84)): (I) is the length of the baseline P is the range (receiver to satellite) dr is the orbit error db is the induced baseline error.

Then the necessary theory is developed in Section 3. In Section 4 we present applications. 2. PRECISION REQUIREMENTS The accuracy of orbits needed to obtain baseline estimates depends mainly on the length of the baseline (see Ref. 4, Eq. r p of a certain accuracy (84)): (I) is the length of the baseline P is the range (receiver to satellite) dr is the orbit error db is the induced baseline error. For GPS satellites we have approximately p = 25000 km . (2) 33 Accepting, for example, db = 1 ca (3) as a maximtF, for the baseline error introduced by the orbit, we obtain the values in Table 1 for the maximunorbit error, dr, allowed.

51/ Considering the essential results of the combination of t e r r e s t r i a l with GPS heights, the d i s t i n c t influence of the GPS heights to the height determination of the network points becomes clear. 5 cm are highly sig- n i f i c a n t improvements of two, t e r r e s t r i a l l y only weakly determined heights. 9. CONCLUSIONS An appropriate combination of the stochastic information obtained from t e r r e s t r i a l and s a t e l l i t e aided network observation requires - a hybrid functional and stochastic model of adjustment - variance component estimation techniques - S-transformation of covariance matrices.

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