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Introduction to the Box Particle Filtering

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Introduction to the Box Particle Filtering. / Gning, Amadou; Ristic, B; Mihaylova, Lyudmila et al.
In: IEEE Signal Processing Magazine, Vol. 30, No. 4, 12.06.2013, p. 166-171.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Gning, A, Ristic, B, Mihaylova, L & Abdallah, F 2013, 'Introduction to the Box Particle Filtering', IEEE Signal Processing Magazine, vol. 30, no. 4, pp. 166-171. https://doi.org/10.1109/MSP.2013.2254601

APA

Gning, A., Ristic, B., Mihaylova, L., & Abdallah, F. (2013). Introduction to the Box Particle Filtering. IEEE Signal Processing Magazine, 30(4), 166-171. https://doi.org/10.1109/MSP.2013.2254601

Vancouver

Gning A, Ristic B, Mihaylova L, Abdallah F. Introduction to the Box Particle Filtering. IEEE Signal Processing Magazine. 2013 Jun 12;30(4):166-171. doi: 10.1109/MSP.2013.2254601

Author

Gning, Amadou ; Ristic, B ; Mihaylova, Lyudmila et al. / Introduction to the Box Particle Filtering. In: IEEE Signal Processing Magazine. 2013 ; Vol. 30, No. 4. pp. 166-171.

Bibtex

@article{9caa820458aa40888646e7c47ff30820,
title = "Introduction to the Box Particle Filtering",
abstract = "This paper presents a novel method for solving nonlinear filtering problems. This approach is particularly appealing in practical situations involving imprecise stochastic measurements, thus resulting in very broad posterior densities. It relies on the concept of a box particle, which occupies a small and controllable rectangular region having a non-zero volume in the state space. Key advantages of the box particle filter (Box-PF) against the standard particle filter (PF) are in its reduced computational complexity and its suitability for distributed filtering. Indeed, in some applications where the sequential importance resampling (SIR) PF may require thousands of particles to achieve an accurate and reliable performance, the Box-PF can reach the same level of accuracy with just a few dozens of box particles.",
keywords = "sequential Monte Carlo methods, Particle methods, uncertainty, Uniform distribution, Box particle filter, tracking",
author = "Amadou Gning and B Ristic and Lyudmila Mihaylova and F. Abdallah",
year = "2013",
month = jun,
day = "12",
doi = "10.1109/MSP.2013.2254601",
language = "English",
volume = "30",
pages = "166--171",
journal = "IEEE Signal Processing Magazine",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

RIS

TY - JOUR

T1 - Introduction to the Box Particle Filtering

AU - Gning, Amadou

AU - Ristic, B

AU - Mihaylova, Lyudmila

AU - Abdallah, F.

PY - 2013/6/12

Y1 - 2013/6/12

N2 - This paper presents a novel method for solving nonlinear filtering problems. This approach is particularly appealing in practical situations involving imprecise stochastic measurements, thus resulting in very broad posterior densities. It relies on the concept of a box particle, which occupies a small and controllable rectangular region having a non-zero volume in the state space. Key advantages of the box particle filter (Box-PF) against the standard particle filter (PF) are in its reduced computational complexity and its suitability for distributed filtering. Indeed, in some applications where the sequential importance resampling (SIR) PF may require thousands of particles to achieve an accurate and reliable performance, the Box-PF can reach the same level of accuracy with just a few dozens of box particles.

AB - This paper presents a novel method for solving nonlinear filtering problems. This approach is particularly appealing in practical situations involving imprecise stochastic measurements, thus resulting in very broad posterior densities. It relies on the concept of a box particle, which occupies a small and controllable rectangular region having a non-zero volume in the state space. Key advantages of the box particle filter (Box-PF) against the standard particle filter (PF) are in its reduced computational complexity and its suitability for distributed filtering. Indeed, in some applications where the sequential importance resampling (SIR) PF may require thousands of particles to achieve an accurate and reliable performance, the Box-PF can reach the same level of accuracy with just a few dozens of box particles.

KW - sequential Monte Carlo methods

KW - Particle methods

KW - uncertainty

KW - Uniform distribution

KW - Box particle filter

KW - tracking

U2 - 10.1109/MSP.2013.2254601

DO - 10.1109/MSP.2013.2254601

M3 - Journal article

VL - 30

SP - 166

EP - 171

JO - IEEE Signal Processing Magazine

JF - IEEE Signal Processing Magazine

IS - 4

ER -