A Novel Gaussian Particle PHD Filter for Multi-Target Tracking

Zeng Jian Huang; Gui Xi Liu; Peng Ju Chang

doi:10.4028/www.scientific.net/AMM.130-134.3143

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 130-134A Novel Gaussian Particle PHD Filter for...

A Novel Gaussian Particle PHD Filter for Multi-Target Tracking

Abstract:

The Gaussian particle probability hypothesis density filter (GPPHDF) needs conventional Monte-Carlo (MC) sampling in predict step and update step, which decreases the accuracy and real-time performance of the algorithm. This paper employs Quasi-Monte-Carlo (QMC) sampling to replace MC sampling, and QMC integration method is introduced to approximating the prediction and update distributions of target states. Hence a tracking algorithm based on the QMC method is proposed, which reduces the computational complexity and improves the accuracy and stability of the tracking algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 130-134)

Pages:

3143-3147

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.130-134.3143

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Online since:

October 2011

Authors:

Zeng Jian Huang, Gui Xi Liu, Peng Ju Chang

Keywords:

Gaussian Particle Probability Hypothesis Density Filter, Multiple Target Tracking, Quasi-Monte-Carlo (QMC), Random Sets

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References

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