t) at the river
’ radar targets
the rectangles
ted position of
/ a vector start-
2 chart several
ie river banks
/nes radar re-
are stored as
e radar reflec-
he rectangles.
2 classified as
ching with the
nd electronic
1 a laser scan-
ndings of the
ow of the ship
asurements in
rs an azimuth
in a distance
m (1 o). The
intages in de-
ip. The least
e radial quan-
m. Thus laser
or the naviga-
or for docking
jracy of laser
le images de-
because both
surroundings.
explained for
for the laser
of the match-
is subject to
aser scanner
on entering a
' black points
ie grey line is
lic chart.
EQUENCES
or banks, the
radar sensor
d buoys and
mation about
0 reconstruct
th respect to
jn to the own
odels has to
lescribing an
1 1 i 1 L L
Figure 7: Example of a laser scanner image
assumed target track. This kind of information can
not be gained from single radar images. It is neces-
sary to interpret the chronological development in im-
age sequences. This goal is achieved by a multiple-
target tracking algorithm [1]. Methods applied are
Kalman filtering [2] for the estimation of target trajec-
tories given the data-association history, and recur-
sive Bayesian tests [8] in order to sequentially inter-
pret the arriving radar image measurements. This
results in a set of contradicting hypotheses, each of
which is associated with a certain probability. The
basic problem originates from the exponential growth
of hypotheses arising if all possible measurement as-
sociations are considered. A detailed description of
the implemented algorithms can be found in [6].
Figure 8 gives an example of a real radar image,
showing one oncoming ship and several radar buoys.
Here we can distinguish two gates for each track, cor-
responding to the update and prediction steps of each
Kalman filter. The line originating at the center of the
prediction gate symbolizes the estimated speed vec-
tor. From this image it can be seen, that by means of
the target tracking technique, a reliable reconstruction
of the navigation scenery can be obtained.
Applying the tracking algorithm to inland shipping,
one has to deal with radar echos arising for exam-
ple from bushes or trees at the river banks or from
bridges. Therefore a classification step is included
in the image processing, where relevant objects for
71
Figure 8: Example for multiple-target tracking meth-
ods
tracking are selected. Within this step, the informa-
tion of the electronic chart is used. The distances
from a radar object to the nearest objects in the chart
provide additional classification information. So it is
possible to decide, whether a radar object is outside
the river and thus irrelevant for tracking. The detec-
tion of radar buoys and landmarks and the treatment
of echos arising from bridges or overhead lines can
also be improved by using the chart information. Thus
the application of chart information improves the effi-
ciency of the tracking algorithm.
6 INTEGRATION OF MEASUREMENTS
The integration of results obtained from different
matching algorithms and the measurements of other
sensors is performed by an extended Kalman filter
[2]. Within the filter, the different measurements are
weighted according to their actual accuracy and an
integrated estimate of the ship's state is computed.
Designing the filter, one has to take into account that
the accuracy of the measurements may vary with
time. Measurements may even be not available tem-
porarily. When for example no landmarks are visible,
the matching of stationary targets and landmarks
cannot be performed. Also some sensors may not
be installed on a specific ship.
In addition to the measurements derived from the