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.

8 PLANNING AN ACTUAL TRAJECTORY

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

74

cumulative risk for each trajectory. Finally, one of

these trajectories is selected as input for the control

task.

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.

References

[1] Blackman, S. S.: Multiple-Target Tracking with

Radar Applications. Artech House, Norwood, MA,

1986.

[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.

35-45.

[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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