Set-Type Belief Propagation with Applications to Poisson Multi-Bernoulli SLAM
Artikel i vetenskaplig tidskrift, 2024

Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is only applicable to the vector-type random variables with a fixed and known number of vector elements, while certain applications rely on random finite sets (RFSs) with an unknown number of vector elements. In this paper, we develop BP rules for factor graphs defined on sequences of RFSs where each RFS has an unknown number of elements, with the intention of deriving novel inference methods for RFSs. Furthermore, we show that vector-type BP is a special case of set-type BP, where each RFS follows the Bernoulli process. To demonstrate the validity of developed set-type BP, we apply it to the Poisson multi-Bernoulli (PMB) filter for simultaneous localization and mapping (SLAM), which naturally leads to a set-type BP PMB-SLAM method, which is analogous to a vector type SLAM method, subject to minor modifications.


Poisson multi-Bernoulli filter

Radio frequency

random finite sets

Filtering algorithms

Filtering theory

Belief propagation

Target tracking

Simultaneous localization and mapping

multi-target tracking

Random variables

simultaneous localization and mapping


Hyowon Kim

Chungnam National University

Angel F. Garcia-Fernsndez

University of Liverpool

Yu Ge

Yuxuan Xia

Linköpings universitet

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

IEEE Transactions on Signal Processing

1053-587X (ISSN) 1941-0476 (eISSN)

Vol. 72 1989-2005






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