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Resampling and Network Theory

journal contribution
posted on 2024-01-29, 00:20 authored by P Choppala, Marcus FreanMarcus Frean, Paul TealPaul Teal
Particle filtering provides an approximate representation of a tracked posterior density which converges asymptotically to the true posterior as the number of particles used increases. The greater the number of particles, the higher the computational complexity. This complexity can be implemented by operating the particle filter in parallel architectures. However, the resampling step in the particle filter requires a high level of synchronization and extensive information interchange between the particles, which impedes the use of parallel hardware systems. This paper establishes a new perspective for understanding particle filtering - that particle filtering can be achieved by adopting the principles of information exchange within a network, the nodes of which are now the particles in the particle filter. We propose to connect particles via a minimally connected network and resample each locally. This strategy facilitates full information exchange among the particles, but with each particle communicating with only a small fixed set of other particles, thus leading to minimal communication overhead. The key benefit is that this approach facilitates the use of many particles for accurate posterior approximation and tracking accuracy.

History

Preferred citation

Choppala, P., Frean, M. & Teal, P. (2022). Resampling and Network Theory. IEEE Transactions on Signal and Information Processing over Networks, 8, 106-119. https://doi.org/10.1109/TSIPN.2022.3146051

Journal title

IEEE Transactions on Signal and Information Processing over Networks

Volume

8

Publication date

2022-01-01

Pagination

106-119

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

Online publication date

2022-01-25

ISSN

2373-776X

eISSN

2373-776X