Multitarget tracking performance metric: deficiency aware subpattern assignment

Oksuz K., CEMGİL A. T.

IET RADAR SONAR AND NAVIGATION, cilt.12, sa.3, ss.373-381, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 3
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1049/iet-rsn.2017.0390
  • Dergi Adı: IET RADAR SONAR AND NAVIGATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.373-381
  • Anahtar Kelimeler: sensor fusion, tracking filters, target tracking, state variables, performance measure, deficiency aware subpattern assignment, conventional data association methods, multitarget tracking algorithms, DASA metric combines three components, tracking filter, Optimal Subpattern Assignment metric, multitarget tracking performance metric, sequential estimation problem, noisy sensor measurements, PROBABILISTIC DATA ASSOCIATION, ALGORITHM, FILTERS, DISTANCE
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

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.