Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member

By Jason L. Williams. IEEE Transactions on Aerospace and Electronic Systems, 2012

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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 multiple hypothesis tracking (MHT). Subsequently, algorithms are obtained by approximating the distribution of associations. Two algorithms result: one nearly identical to joint integrated probabilistic data association (JIPDA), and another related to the multiple target multi-Bernoulli (MeMBer) filter. Both improve performance in challenging environments.

Subfields: Artificial Intelligence, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/af37b28b022b0cecf5c931d1e00d7ee9887a5cae