Multitarget tracking performance metric: deficiency aware subpattern assignment

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 As...

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Bibliographic Details
Published in:IET radar, sonar & navigation sonar & navigation, 2018-03, Vol.12 (3), p.373-381
Main Authors: Oksuz, Kemal, Cemgil, Ali Taylan
Format: Article
Language:English
Subjects:
conventional data association methods
DASA metric combines three components
deficiency aware subpattern assignment
multitarget tracking algorithms
multitarget tracking performance metric
noisy sensor measurements
Optimal Subpattern Assignment metric
performance measure
Research Article
sensor fusion
sequential estimation problem
state variables
target tracking
tracking filter
tracking filters
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Summary: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.
ISSN:1751-8784
1751-8792
1751-8792
DOI:10.1049/iet-rsn.2017.0390