Generalized optimal sub-pattern assignment metric
Paper in proceedings, 2017

This paper presents the generalized optimal sub-pattern assignment (GOSPA) metric on the space of finite sets of targets. Compared to the well-established optimal sub-pattern assignment (OSPA) metric, GOSPA is not normalised by the cardinality of the largest set and it penalizes cardinality errors differently, which enables us to express it as an optimisation over assignments instead of permutations. An important consequence of this is that GOSPA allows us to penalize localization errors for detected targets and the errors due to missed and false targets, as indicated by traditional multiple target tracking (MTT) performance measures, in a sound manner. In addition, we extend the GOSPA metric to the space of random finite sets, which is important to evaluate MTT algorithms via simulations in a rigorous way.

optimal sub-pattern assignment metric

random finite sets

Multiple target tracking



Abu Sajana Rahmathullah


Angel Garcia

Aalto University

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

20th International Conference on Information Fusion, Fusion 2017, Xian, China, 10-13 July 2017


Subject Categories

Control Engineering





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