Hybrid particle/extended finite memory filter to improve target tracking accuracy of radar measurement in harsh environments

Chang Joo Lee, Jong Young Won, Dong Sung Pae, Myo Taeg Lim

Research output: Contribution to journalArticlepeer-review

Abstract

Although the particle filter (PF) generally provides accurate estimation, it fails in harsh environments, such as severe signal noise and/or abrupt state change. The PF also requires a number of particles for accurate estimation, causing heavy com-putational burden. Therefore, it is difficult to use the PF for real applications. To overcome PF drawbacks, we propose the extended finite memory (EFM) filter and hybrid particle filtering algorithm combining the regularized particle filter (RPF) as the main filter with an auxiliary EFM filter. The hybrid particle/EFM filter can detect RPF failure and reset the particles using an EFM estimation. The proposed filter shows robust performance against severe signal noise and abrupt change of target motion. Experiments using vehicle radar signals were performed in harsh environments to compare the proposed tracker with current best practice regularized particle and extended Kalman trackers.

Original languageEnglish
Pages (from-to)1749-1758
Number of pages10
JournalJournal of Electrical Engineering and Technology
Volume14
Issue number4
DOIs
Publication statusPublished - 2019 Jul

Keywords

  • Finite impulse response structure
  • Hybrid filter
  • Particle filter
  • Radar measurement
  • Target tracking

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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