Target tracking based on interactive multiple models adaptive probabilistic data association algorithm

Hui Li, An Zhang, Ying Shen, Sheng Qiang He, Cheng Cheng

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Based on the idea of the combined interactive multiple models probabilistic data association (IMMPDA) algorithm, the author brought an adaptive filtering method into the probabilistic data association(PDA) filter and put forward a novel algorithm - Interactive Multiple Models Adaptive Probabilistic Data Association(IMM-APDA) algorithm, which had an efficient combination of the state estimation with the data association. The tracking extension was spread and the accuracy of maneuvering target tracking in cluttered environment can be improved by this new algorithm, which bypassed the choice of different multiple models. The computational simulation results indicate that IMM-APDA algorithm will decrease the computational burden and has a better performance than IMMPDA in tracking maneuvering target in clutter.

Original languageEnglish
Pages (from-to)172-176
Number of pages5
JournalChinese Journal of Sensors and Actuators
Volume20
Issue number1
StatePublished - Jan 2007

Keywords

  • Adaptive probabilistic data association
  • Monte Carlo simulation
  • Multiple models
  • Radar data processing
  • State estimation

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