An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA

TL;DR

This paper compares different multi-target metrics (GOSPA, OSPA, UOSPA) for sensor management, revealing that GOSPA allows independent sensor actions while OSPA and UOSPA lead to entangled decisions due to the spooky effect.

Contribution

It provides an analytical comparison of GOSPA versus OSPA and UOSPA metrics in multi-target sensor management, highlighting their different implications for sensor coordination.

Findings

GOSPA enables independent sensor actions in management.

OSPA and UOSPA cause entangled sensor decisions due to the spooky effect.

The analysis clarifies metric impacts on sensor management strategies.

Abstract

This paper presents an analysis on sensor management using a cost function based on a multi-target metric, in particular, the optimal subpattern-assignment (OSPA) metric, the unnormalised OSPA (UOSPA) metric and the generalised OSPA (GOSPA) metric (\alpha=2). We consider the problem of managing an array of sensors, where each sensor is able to observe a region of the surveillance area, not covered by other sensors, with a given sensing cost. We look at the case in which there are far-away, independent potential targets, at maximum one per sensor region. In this set-up, the optimal action using GOSPA is taken for each sensor independently, as we may expect. On the contrary, as a consequence of the spooky effect at a distance in optimal OSPA/UOSPA estimation, the optimal actions for different sensors using OSPA and UOSPA are entangled.