Multitarget tracking performance metric: deficiency aware subpattern assignment

Date

2018-03-01

Author

Oksuz, Kemal
CEMGİL, ALİ TAYLAN

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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.

Subject Keywords

Sensor fusion, Tracking filters, Target tracking, State variables, Performance measure, Deficiency aware subpattern assignment, Optimal Subpattern Assignment metric, Tracking filter, DASA metric combines three components, Multitarget tracking algorithms, Conventional data association methods, Noisy sensor measurements, Sequential estimation problem, Multitarget tracking performance metric

URI

https://hdl.handle.net/11511/65599

Journal

IET RADAR SONAR AND NAVIGATION

DOI

https://doi.org/10.1049/iet-rsn.2017.0390

Collections

Department of Computer Engineering, Article

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Citation Formats

K. Oksuz and A. T. CEMGİL, “Multitarget tracking performance metric: deficiency aware subpattern assignment,” IET RADAR SONAR AND NAVIGATION, pp. 373–381, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65599.