A Bayesian network tracking database
Fritz H. Obermeyer, Aubrey B. Poore · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Most maximum likelihood (ML) trackers based on measurement fusion (measurement-to-measurement or measurement-to-track) or track-to-track fusion produce a single data association hypothesis together with kinematic track state estimates. Uncertainty in the track states due to process and measurement noise is represented by covariance matrices, however uncertainty in the data association is either entirely neglected or representative of only limited types of association uncertainty. This paper presents a Bayesian-Network uncertainty management system for use in conjunction with maximum-likelihood trackers. The system, termed the Bayesian Network Tracking Database (BNTD) comprises algorithms for interactive access, whereby expectation values of arbitrary track properties can be calculated over all association hypotheses, and algorithms and data-structures for long-term storage, whereby the complete set of association hypotheses can be efficiently approximated, even over long time intervals. A conjoined MLE/BNTD system is thus capable of supporting target identification (ID), feature-aided tracking, and long-term track maintenance (LTTM).