stanbul 2004
ne indepen-
is based on
ne indepen-
vation mod-
a particular
sonable de-
nation soft-
simple. We
proach
he class we
ed paradigm.
ious theory,
rk determi-
vare system
s: observa-
-olomina et
ing in time
(time epoch
ed observa-
vations, al-
pendent as
n principle,
' dependent
ze time de-
Yodels from
ation mod-
proach, the
h different
to internal
cover, with
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1 designed]
n more in-
nputational
the estima-
ftware im-
her words,
NA and an
m is possi-
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part Bl. Istanbul 2004
7.3 A unified exploitation approach
Unified theoretic frameworks lead to simple and efficient
algorithms and software. Unified software approaches lead
to simple and efficient exploitation procedures. In partic-
ular, an eventual software implementation of the concepts
presented, would lead to common shareable input/output
formats for a number of estimation engines.
A benefit of a unified approach is that we can follow dif-
ferent strategies and that we can combine them. In some
situations, one approach should be preferred. In other situ-
ations we can combine them. For a family of problems, one
approach may be preferred for calibration tasks whereas
the other may be preferred for orientation tasks.
Note, as mentioned in section 1, that the output estimated
parameters of a static network may be used as input obser-
vations for a time dependent network. Similarly, an SSA
engine can be used to generate initial approximations for a
NA engine. In all the cases, it is clear that interoperability
is easier to achieve with a unified approach.
8 CONCLUSION, ONGOING WORK AND
FURTHER RESEARCH
In this paper we have defined in a precise way the con-
cept of time dependent networks. The proposed concept
extends the classical unified (from geodesy, photogramme-
try and remote sensing) geomatic concept of network. In
short, a time dependent network is a classical network that
incorporates stochastic processes —that we call time de-
pendent parameters— and dynamic models —that we call
dynamic observation models. We have related time depen-
dent networks and their solution approaches to the exist-
ing Kalman filtering/smoothing and network methodolo-
gies —what we call the SSA and the NA solution appro-
aches— and have discussed their advantages and disadvan-
tages. Last, we have given some hints on how this unified
approach can be exploited at the software development and
data processing levels.
We are currently developing an experimental software pro-
totype that implements the concepts presented in this pa-
per. Further research will be related to the numerical so-
lution of SDEs for geomatic applications and to their op-
timization in terms of speed and memory/disk storage re-
quirements.
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ACKNOWLEDGEMENTS
The research reported in this paper has been performed
within the frame of the ITAVERA project that the Institute
of Geomatics is conducting for GeoNumerics and with par-
tial support of the Spanish Ministry of Science and Tech-
nology, through the OTEA-g project of the Spanish Na-
tional Space Research Programme (reference: ESP2002-
03687).
