Abstract
The PMHT (probabilistic multihypothesis tracker) uses soft a posteriori probability associations between measurements and targets. Its implementation is a straightforward iterative application of a Kalman smoother operating on (i.e., modified) measurements, and of recalculation of these synthetic measurements based on the current track estimate. In this correspondence, we first discuss the basic PMHT and some of the older PMHT variants that have been used to enhance convergence. We then introduce the new turbo PMHT, which is informed by the recent success of turbo decoding in the digital communication context. This new PMHT has performance substantially improved versus any of the previous versions.
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