Particle tracking with iterated Kalman filters and smoothers: the PMHT algorithm

We introduce the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm f or particle tracking in high-energy physics detectors. This algorithm has b een developed recently for tracking multiple targets in clutter, and it is based on maximum likelihood estimation with help of the EM algorithm. The r esulting algorithm basically consists of running several iterated and coupl ed Kalman filters and smoothers in parallel. It is similar to the Elastic A rms algorithm, but it possesses the additional feature of being able to tak e process noise into account, as for instance multiple Coulomb scattering. Herein, we review its basic properties and derive a generalized version of the algorithm by including a deterministic annealing scheme. Further develo pments of the algorithm in order to improve the performance are also discus sed. In particular, we propose to modify the hit-to-track assignment probab ilities in order to obtain competition between hits in the same detector la yer. Finally, we present results of an implementation of the algorithm on s imulated tracks from the ATLAS Inner Detector Transition Radiation Tracker (TRT). (C) 1999 Elsevier Science B.V. All rights reserved.