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Classical multi-target tracking consists of three sub-problems; detection, association, and estimation [39, 28].
The data association and estimation algorithm described in the previous sections was applied to 10 sets of measured data.
The data association and estimation algorithm described in the previous sections was applied to 10 sets of measurement data.
Applications demonstrated by simulation include phenotype and effect prediction and association, and estimation of heritability and classical variance components.
Object tracking in networked systems is a widely used scenario to assess newly designed methods in the field of association and estimation.
Based on TSSG tracking, however, data association and track trajectory estimation can be performed as a follow-up step, whereby track association and estimation errors do not propagate back to the TSSG tracker.
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Based on TSSG state estimates, more informative target position and track estimates can be obtained in a follow-up step, ensuring that track association and position estimation errors do not propagate back into TSSG state estimates.
Generally, its tracking procedure can be divided into two steps: data association and state estimation.
The results will not be fed back to the TSSG trackers, thus ensuring resilience of TSSG estimates to data mis-association and track estimation errors.
Because of the need to account for the complex survey design when estimating variances and confidence intervals, SUDAAN was used in modeling of associations and variance estimation [ 32].
We observed the strongest associations (and smallest estimation errors) between HR and TPE recorded 1 10 hr before CV measurements.
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