Together with the unknown ARI process paramters aı we obtain a 
highly nonlinear estimation problem which can be solved in an 
iterative computation scheme (Lindenberger 1987). The condi- 
tions to be fulfilled by the data for successfull solution of 
the GM model and the VCE are mentioned in the same paper (e.g. 
the variances on 2 and ce? have to be of same order of magni- 
tude). 
Gross errors in the observations would disturb the validity of 
the GM model in eq.(2). These gross errors are automatically 
detected and are taken into consideration by individual weights 
in eq(2). The weights are calculated following the robust esti- 
mation theory (Danish method, Krarup et al. 1980). On the other 
hand, a robust treatment of eq. (3) reduces the influences of 
edges and discontinuities in the data, which disturb the ARI 
model. 
2.3 Capability of the algorithm 
What are the main results from the algorithm ? Under the 
assumption that the true trajectory of the sensor platform can 
be modelled by an ARI-process, we obtain a filtered data set x: 
which is the most probable representation of the true track. 
Together with the estimated ARI process parameters, several 
demands of further evaluation of the sensor data will be 
satisfied. 
In addition, the ARI-model yields important results, relevant 
for the analysis of the stochastic model. The estimated vari- 
ance of the observation noise On 2 describes the observation 
process. The variance of the prediction errors oe? gives a 
fidelity measure how well the the ARI-model is suited for the 
real physical process. The inversion of the normal equation 
system out of equations (2) and (3) provides accuracy criteria 
of the filtered data set, especially the variance of the fil- 
tered data ox 2 and the autocorrelation coefficients r(h). It is 
emphasised here that all stochastic results are obtained 
without any a priori information. 
Any systematic effects in the time-series, such as drifts of 
the orientation parameters with time, cannot be taken into 
consideration by the algorithm. For this reason the estimated 
accuracies must be understood not as absolute but as relative 
accuracies. 
3. Applications 
3.1 Position coordinates from NAVSTAR-GPS 
The NAVSTAR Global Positioning System GPS enables the determi- 
nation of the x,y,z position coordinates of one or more GPS 
receivers. The application of GPS is of particular interest in 
photogrammetry for the inflight positioning of the aerial 
camera. This reduces drastically the ground control require- 
ments for aerial triangulation (Friep 1986). 
In the case of a stationary GPS receiver the accuracy estima- 
tion of GPS measurements is relatively simple due to the redun- 
dancy of the observations. In contrast to this, the application 
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