Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer
Provides a theoretical unification of RFS-based tracking and traditional data association methods, offering improved algorithms for multi-target tracking.
The paper derives a full Bayes RFS filter that reveals implicit data association akin to MHT, and obtains two approximations: one nearly identical to JIPDA and another related to MeMBer, both improving tracking performance in challenging environments.
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.