Set-Type Belief Propagation With Applications to Poisson Multi-Bernoulli SLAM

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, w...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on signal processing Vol. 72; pp. 1989 - 2005
Main Authors Kim, Hyowon, Garcia-Fernandez, Angel F., Ge, Yu, Xia, Yuxuan, Svensson, Lennart, Wymeersch, Henk
Format Journal Article
LanguageEnglish
Published New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Belief propagation
Filtering algorithms
Filtering theory
multi-target tracking
Poisson multi-Bernoulli filter
Probabilistic inference
Radio frequency
random finite sets
Random variables
Sequences
Simultaneous localization and mapping
Statistical analysis
Target tracking
Vectors
Radio frequency
Simultaneous localization and mapping
Target tracking
random finite sets
multi-target tracking
Filtering algorithms
Vectors
Filtering theory
Random variables
Poisson multi-Bernoulli filter
Belief propagation
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1053-587X
1941-0476
1941-0476
DOI:10.1109/TSP.2024.3383543