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Systems for data processing, anaylsis and representation

algorithms. The matching algorithms themselves are
part of the block comparison in figure 10. The dif-
ference signals are weighted according to the actual
situation and the accuracy of the measurements and
are used as corrections on the model world. This is
for example performed within the update steps of the
Kalman filter discussed in section 6. Thus the model
world is driven to converge towards the real world.
The advantage of this model based processing
scheme stems from the fact that the model world sim-
ulated on the computer provides considerably more
detailed information of the ship and its environment
than the real world sensors themselves. As explained
in the previous sections, a precise estimate of the own
ship's position, a detailed reconstruction of the navi-
gation environment and a dependable observation of
the actual traffic situation can be obtained from the
sensor processing level. Therefore all information
required at the control level is provided. The most
important task of this level is the trajectory genera-
tion for the own ship determining the control inputs to
engine and rudder.
Trajectory generation is done in two steps: First,
an optimal trajectory for upstream and downstream
travel is computed off-line, assuming the absence of
foreign ships. However, time invariant environmental
constraints as well as ship dependent dynamics are
taken into account. In addition to this, it is possible to
compute a set of trajectories for different water levels.
These trajectories are stored in the electronic chart as
ideal guiding lines.
For the case that the actual traffic situation results
in interferences with other ships, a second step of
on-line trajectory recomputation has to follow in the
sequel. This second step uses the ideal guiding lines
and the limits of navigable water as well as traffic
rules stored in the chart. This a-priori information is
combined with the results of the multiple-target track-
ing algorithm. Here the navigator may also interact,
telling the system how foreign ships are to be passed
and how they should be encountered. This allows
to incorporate information resulting from the commu-
nication with navigators of other ships or from other
external sources.
All these sources of information are combined in the
on-line computation. The algorithm employed starts
from a coarse grid superimposed over the waterway.
A risk function is assigned to every point in the grid.
This function consists of a constant part derived from
the chart information and a time-varying part repre-
senting the results of the multiple-target tracking al-
gorithm. A foreign ship is taken into account at a
predicted place of encounter. The second step in the
on-line calculations is a search algorithm resulting in
possibly several trajectories through the grid and a
cumulative risk for each trajectory. Finally, one of
these trajectories is selected as input for the control
The control task is implemented as a linear state
controller designed for variable command control [3].
This task generates the signals acting on the rudder
and engine throttle. For large ships in narrow canals
this controller is not sufficient. Current research fo-
cuses on the development of new control concepts
incorporating results from nonlinear and predictive
control theory.
[1] Blackman, S. S.: Multiple-Target Tracking with
Radar Applications. Artech House, Norwood, MA,
[2] Gelb, A. (Hrsg.): Applied Optimal Estimation. The
M.I.T. Press, Cambridge, Massachusetts, 1974.
[3] Gilles, E. D., Neul, R., Plocher, T., und Kabatek,
U.: Ein integriertes Navigationssystem für Bin-
nenschiffe. Automatisierungstechnik 38 (1990),
S. 202-209, 247—257.
[4] Kabatek, U., Sandler, M., Neul, H., und Gilles,
E. D.: Eine elektronische FluBkarte als Wissens-
basis in einem integrierten Navigationssystem.
Zeitschrift für Vermessungswesen 117 (1992), S.
[b] Neul, R.: Positionsbestimmung eines navigieren-
den Schiffes durch kartengestützte Radarbildver-
arbeitung, volume 323 of Fortschittsberichte VDI
Reihe 8. VDI-Verlag, Düsseldorf, 1993.
[6] Plocher, T.: Einsatz von Kalman-Filtern und
Bayesschen Schátzverfahren zur Verfolgung be-
wegter Objekte in Bildsequenzen, volume 320
of Fortschittsberichte VDI Reihe 8. VDI-Verlag,
Düsseldorf, 1993.
[7] Plocher, T. und Gilles, E. D.: Rekursive Objek-
tverfolgung in Bildsequenzen. Automatisierung-
stechnik 40 (1992), S. 14—20,59-63.
[8] Reid, D.: An algorithm for tracking multiple tar-
gets. IEEE Trans. Automat. Contr. 24(12) (1979),
S. 843-854.
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