A Novel Gaussian Particle PHD Filter for Multi-Target Tracking

Abstract:

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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.

Info:

Periodical:

Edited by:

Han Zhao

Pages:

3143-3147

DOI:

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

Citation:

Z. J. Huang et al., "A Novel Gaussian Particle PHD Filter for Multi-Target Tracking", Applied Mechanics and Materials, Vols. 130-134, pp. 3143-3147, 2012

Online since:

October 2011

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Price:

$35.00

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