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

We use the two-dimensional track-to-track assignment problem to analyze the trade-off between accuracy and runtime performance for three methods of estimating track-to-track assignment probabilities: enumeration of the k-best data association hypotheses, importance sampling (IS), and Markov chain Monte Carlo (MCMC) estimation. We also characterize the appropriate operating regime for each method.