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.

Tracking

track coalescence

MMOSPA

target identity

Author

David F. Crouse

P. Willett

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Daniel Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Marco Guerriero

14th International Conference on Information Fusion, Fusion 2011; Chicago, IL; 5 July 2011 through 8 July 2011

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

ISBN

978-145770267-9

More information

Created

10/7/2017