Application of adaptive reduced sigma points unscented Kalman filter to the tracking of maneuvering target

Zhan Xin Zhou*, Jia Bin Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Based on the principle of statistical linear regression, a set of n+2 sigma points instead of 2n+1 sigma points used in the unscented Kalman filter (UKF), is constructed to approximate the system state. And filter accuracy is second order. Real-time of modified UKF is improved. In order to describe accurately the maneuvering target, the current statistical model is used. And the equation of acceleration error covariance is modified at every sample time of the filter. The modified adaptive UKF is presented for estimating the position, velocity and acceleration of maneuvering target. Monte Carlo simulations show the modified adaptive UKF acquires good performance for tracking position of maneuvering target. The modified adaptive UKF has better computational efficiency than UKF.

Original languageEnglish
Pages (from-to)74-77
Number of pages4
JournalJournal of Beijing Institute of Technology (English Edition)
Volume16
Issue number1
Publication statusPublished - Mar 2007

Keywords

  • Adaptive UKF
  • Maneuvering target tracking
  • Nonlinear filter
  • Reduced sigma point

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Zhou, Z. X., & Chen, J. B. (2007). Application of adaptive reduced sigma points unscented Kalman filter to the tracking of maneuvering target. Journal of Beijing Institute of Technology (English Edition), 16(1), 74-77.