Probability Evaluation in MHT with a Product Set Representation of Hypotheses

Multiple hypothesis tracking algorithms that rely on hypothesis probabilities for pruning typically generates the n-best global hypotheses. In some cases, the probability mass is diffuse in the space of global hypotheses and a large n is desirable, implying a high computational demand. In this work, we present an alternative method for evaluation of hypothesis probabilities. Global hypotheses are then represented with exclusive product sets. Each product set has the potential of representing many global hypotheses. A method that generates the product sets is introduced, including a recursive formulation for computational tractability. In numerical evaluations, the method is compared to an optimization based method that generates the n-best hypotheses. Both an improved ability of representation, and a reduced computational demand are demonstrated in a constructed example