Riemann–Langevin Particle Filtering in Track-Before-Detect

Fernando Iglesias Garcia; Pranab K. Mandal; Melanie  Bocquel; Antonio Garcia Marques

doi:10.1109/LSP.2018.2841507

Riemann–Langevin Particle Filtering in Track-Before-Detect

Fernando Iglesias Garcia (Corresponding Author), Pranab K. Mandal, Melanie Bocquel, Antonio Garcia Marques

Research output: Contribution to journal › Article › Academic › peer-review

15 Citations (Scopus)

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Abstract

Track-before-detect (TBD) is a powerful approach that consists in providing the tracker directly with the sensor measurements without any predetection. Due to the measurement model nonlinearities, online state estimation in TBD is most commonly solved via particle filtering. Existing particle filters for TBD do not incorporate measurement information in their proposal distribution. The Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to exploit all available knowledge of the posterior (that is, both prior and measurement information). This letter synthesizes recent advances in differential-geometric LMC-based filtering to introduce its application to TBD. The benefits of LMC filtering in TBD are illustrated in a challenging low-noise scenario.

Original language	English
Pages (from-to)	1039-1043
Number of pages	5
Journal	IEEE signal processing letters
Volume	25
Issue number	7
Early online date	28 May 2018
DOIs	https://doi.org/10.1109/LSP.2018.2841507
Publication status	Published - Jul 2018

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10.1109/LSP.2018.2841507

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Riemann-Langevin Particle Filtering in Track-Before-Detect
Iglesias-Garcia, F. J., Mandal, P. K., Bocquel, M. & Marques, A. G., 1 May 2017, ArXiv.org.
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abstract = "Track-before-detect (TBD) is a powerful approach that consists in providing the tracker directly with the sensor measurements without any predetection. Due to the measurement model nonlinearities, online state estimation in TBD is most commonly solved via particle filtering. Existing particle filters for TBD do not incorporate measurement information in their proposal distribution. The Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to exploit all available knowledge of the posterior (that is, both prior and measurement information). This letter synthesizes recent advances in differential-geometric LMC-based filtering to introduce its application to TBD. The benefits of LMC filtering in TBD are illustrated in a challenging low-noise scenario.",

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DO - 10.1109/LSP.2018.2841507

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JF - IEEE signal processing letters

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ER -

Riemann–Langevin Particle Filtering in Track-Before-Detect

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Riemann-Langevin Particle Filtering in Track-Before-Detect

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