The Set MHT
Paper in proceedings, 2011
Abstract—We introduce the Set MHT, a tracking algorithm
that maintains multiple hypotheses and produces “smooth”
estimates without the track coalescence often associated with
Minimum Mean Squared Error (MMSE) estimation or the
jitter associated with Maximum Likelihood (ML) estimation.
It does this by utilizing Minimum Mean Optimal Subpattern
Assignment (MMOSPA) estimation techniques coupled with a
theoretically-grounded approach for probabilistically determining
the identities of the state estimates. Unlike traditional MHT
algorithms, the Set MHT does not “forget” uncertainty in target
identities, i.e. display an unjustifiably high confidence level in
the target identities, as a result of pruning out competing
hypotheses. Rather, it uses merging techniques while avoiding the
shortcomings of traditional Gaussian mixture reduction trackers.