Multitarget tracking performance metric: deficiency aware subpattern assignment


Oksuz K., CEMGİL A. T.

IET RADAR SONAR AND NAVIGATION, cilt.12, ss.373-381, 2018 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 12 Konu: 3
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1049/iet-rsn.2017.0390
  • Dergi Adı: IET RADAR SONAR AND NAVIGATION
  • Sayfa Sayıları: ss.373-381

Özet

Multitarget tracking is a sequential estimation problem where conditioned on noisy sensor measurements, state variables of several targets need to be estimated recursively. In this study, the authors propose a novel performance measure for multitarget tracking named as Deficiency Aware Subpattern Assignment (DASA), that can be used to consistently compare algorithms in a broad spectrum of formulations ranging from conventional data association methods to random finite set based multitarget tracking algorithms. The DASA metric combines three components (localisation, type 1 and type 2 errors) in order to represent the behaviour of the tracking filter coherently. Furthermore, a Monte Carlo method is proposed in order to set the cut-off parameter for the case that the measurement model is known. They illustrate in their simulations that DASA improves upon the previously proposed Optimal Subpattern Assignment metric.