PROJECT TITLE :

A Comparison of Methods for Estimating Track-to-Track Assignment Probabilities

ABSTRACT:

We have a tendency to use the two-dimensional track-to-track assignment downside to investigate the trade-off between accuracy and runtime performance for 3 strategies of estimating track-to-track assignment possibilities: enumeration of the $k$-best knowledge association hypotheses, importance sampling (IS), and Markov chain Monte Carlo (MCMC) estimation. We additionally characterize the acceptable operating regime for each method.


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