In this paper, we present an algorithm which lies in the domain of task
allocation for a set of static autonomous radars with rotating antennas. It
allows a set of radars to allocate in a fully decentralized way a set of active
tracking tasks according to their location, considering that a target can be
tracked by several radars, in order to improve accuracy with which the target
is tracked. The allocation algorithm proceeds through a collaborative and fully
decentralized auction protocol, using a collaborative auction protocol
(Consensus Based Bundle Auction algorithm). Our algorithm is based on a double
use of our allocation protocol among the radars. The latter begin by allocating
targets, then launch a second round of allocation if theyhave resources left,
in order to improve accuracy on targets already tracked. Our algorithm is also
able to adapt to dynamism, i.e. to take into account the fact that the targets
are moving and that the radar(s) most suitable for Tracking them changes as the
mission progresses. To do this, the algorithm is restarted on a regular basis,
to ensure that a bid made by a radar can decrease when the target moves away
from it. Since our algorithm is based on collaborative auctions, it does not
plan the following rounds, assuming that the targets are not predictable enough
for this. Our algorithm is however based on radars capable of anticipating the
positions of short-term targets, thanks to a Kalman filter. The algorithm will
be illustrated based on a multi-radar tracking scenario where the radars,
autonomous, must follow a set of targets in order to reduce the position
uncertainty of the targets. Standby aspects will not be considered in this
scenario. It is assumed that the radars can pick up targets in active pursuit,
with an area ofuncertainty corresponding to their distance