A new computational method for ambiguity assessment of solutions to assignment problems arising in target tracking

摘要

We present a new approach to estimating the probability of each association in a 2D assignment problem defined by likelihood ratios. Our method divides the set of feasible hypotheses into clusters, and converts a collection of hypotheses into a collection of clusters containing them, reducing the variance of the estimate. Simulations show that our method often generates substantially more accurate probability estimates in less time than traditional methods. Our method can obtain reasonably accurate probabilities of association based on only the input cost matrix and single best candidate solution, eliminating the need for a K-best solution method or MCMC sampling.