Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer
Systems and Control
2016-08-25 v6 Computer Vision and Pattern Recognition
Abstract
Recent developments in random finite sets (RFSs) have yielded a variety of tracking methods that avoid data association. This paper derives a form of the full Bayes RFS filter and observes that data association is implicitly present, in a data structure similar to MHT. Subsequently, algorithms are obtained by approximating the distribution of associations. Two algorithms result: one nearly identical to JIPDA, and another related to the MeMBer filter. Both improve performance in challenging environments.
Cite
@article{arxiv.1203.2995,
title = {Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer},
author = {Jason L. Williams},
journal= {arXiv preprint arXiv:1203.2995},
year = {2016}
}
Comments
Journal version at http://ieeexplore.ieee.org/document/7272821. Matlab code of simple implementation included with ancillary files