[Back]

T. Kropfreiter, F. Hlawatsch:
"A Probabilistic Label Association Algorithm for Distributed Labeled Multi-Bernoulli Filtering";
Talk: IEEE 23rd International Conference on Information Fusion (FUSION-20), Rustenburg, South Africa; 07-06-2020 - 07-09-2020; in: "FUSION-20", (2020), # - ?.

We consider a distributed labeled multi-Bernoulli
(LMB)filter that uses the generalized covariance intersection
technique for fusing the local LMB distributions. A critical
aspect of such filters is to correctly associate labeled Bernoulli
components describing the same object at different sensors. Here,
we improve on previously proposed association schemes by introducing a probabilistic framework and algorithm for object(label)
association. Instead of enforcing a hard association, we propose
to compute association probabilities and use them in the fusion
of the LMB posterior distributions. To develop our probabilistic
label association scheme, we first derive a formulation of the
fused multi object distribution that involves a label association
distribution. We then show that approximating the label association distribution by the product of its marginals results in a fused multi object distribution that is again of LMB type. An efficient LMB fusion algorithm is finally obtained by using a belief propagation scheme for fast approximate marginalization and a Gaussian approximation. Simulation results demonstrate that the resulting distributed LMB filter outperforms astate-of-the-art method using hard label association.

http://dx.doi.org/10.23919/FUSION45008.2020.9190440

https://publik.tuwien.ac.at/files/publik_292192.pdf