Paper Title:
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.

  Info
Periodical
Edited by
Han Zhao
Pages
3143-3147
DOI
10.4028/www.scientific.net/AMM.130-134.3143
Citation
Z. J. Huang, G. X. Liu, P. J. Chang, "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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