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Spezial:bib:2011_sks C_fusion.pdf

2011_sksc_fusion.pdf
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🔗 Source: cvg.cit.tum.de
📊 Size: 1.38 MB
📄 Pages: 8 pages
⬇️ Downloads: 71

Summary

The authors present a sequential Monte Carlo (SMC) implementation of the intensity filter (iFilter) for multi-target tracking, which estimates the number of targets and their states from a sequence of observations in the presence of detection uncertainty, association uncertainty, and clutter. The iFilter is a generalization of the probability hypothesis density (PHD) filter and can estimate a clutter model from measurements, eliminating the need for a-priori knowledge of clutter intensity. The SMC-iFilter is derived in six steps, and its performance is analyzed on simulated and real data, showing that it outperforms the PHD filter when the clutter intensity is unknown or incorrectly chosen.

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Description

The authors present a sequential Monte Carlo (SMC) implementation of the intensity filter (iFilter) for multi-target tracking, which estimates the number of...

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  • File Format: PDF
  • File Size: 1.38 MB
  • Pages: 8
  • Language: EN
  • Total Downloads: 71
  • Last Updated: 1 month ago

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