  In this paper, we show the spooky effect at a distance that arises in optimal
estimation of multiple targets with the optimal sub-pattern assignment (OSPA)
metric. This effect refers to the fact that if we have several independent
potential targets at distant locations, a change in the probability of
existence of one of them can completely change the optimal estimation of the
rest of the potential targets. As opposed to OSPA, the generalised OSPA (GOSPA)
metric ($\alpha=2$) penalises localisation errors for properly detected
targets, false targets and missed targets. As a consequence, optimal GOSPA
estimation aims to lower the number of false and missed targets, as well as the
localisation error for properly detected targets, and avoids the spooky effect.

Signal Processing;Computer Vision and Pattern Recognition;Machine Learning