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  • 标题:Variable-Mass Particle Filter for Road-Constrained Vehicle Tracking
  • 本地全文:下载
  • 作者:Giorgos Kravaritis ; Bernard Mulgrew
  • 期刊名称:EURASIP Journal on Advances in Signal Processing
  • 印刷版ISSN:1687-6172
  • 电子版ISSN:1687-6180
  • 出版年度:2008
  • 卷号:2008
  • DOI:10.1155/2008/321967
  • 出版社:Hindawi Publishing Corporation
  • 摘要:

    The paper studies the road-constrained vehicle tracking problem employing the multiple-model particle filtering framework. It introduces an approach which enables for a more efficient particle use within the multimodel structure of the tracker; rather than allocating the particles to the various modes of operation using fixed mode probabilities, it proposes to allocate the particles freely according to user-defined application-specific criteria. For compensating for the arbitrary allocation of the particles, the particles are assigned with masses which scale appropriately their weights. Simulation results demonstrate the improved particle efficiency of the new variable-mass approach when contrasted with the standard variable-structure multiple model particle filter in a vehicle tracking application.

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