Shooting two birds with two bullets: how to find Minimum Mean OSPA estimates
Paper i proceeding, 2010
Most area-defense formulations follow
from the assumption that threats must first be identified
and then neutralized. This is reasonable, but inherent
to it is a process of labeling: threat A must be identified
and then threat B, and then action must be taken.
This manuscript begins from the assumption that such
labeling (A & B) is irrelevant. The problem naturally
devolves to one of Random Finite Set (RFS) estimation:
we show that by eschewing any concern of target
label we relax the estimation procedure, and it is perhaps
not surprising that by such a removal of constraint (of
labeling) performance (in terms of localization) is enhanced.
A suitable measure for the estimation of unlabeled
objects is the Mean OSPA (MOSPA). We derive a
general algorithm which provided the optimal estimator
which minimize the MOSPA. We call such an estimator
a Minimum MOSPA (MMOSPA) estimator.