Adaptive filtering for stochastic systems with generalized disturbance inputs

Yan Liang, Donghua Zhou, Lei Zhang, Quan Pan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)

Abstract

This letter presents a new class of discrete-time linear stochastic systems with the statistically-constrained disturbance input, which can represent an arbitrary linear combination of dynamic, random, and deterministic disturbance inputs to generalize the complicated modeling error encountered in actual applications. An adaptive filtering scheme is proposed for such systems by recursively constructing and adaptively minimizing the upper-bounds of covariance matrices of the state predictions, innovations, and estimates. The minimum-upper-bound filter is then obtained via online scalar convex optimization. The experiment on maneuvering target tracking shows that the proposed filter can significantly reduce the peak estimation errors due to maneuvers, compared with the well-known IMM method.
Original languageEnglish
Pages (from-to)645-648
Number of pages4
JournalIEEE Signal Processing Letters
Volume15
DOIs
Publication statusPublished - 1 Dec 2008

Keywords

  • Adaptive Kalman filtering
  • Discrete time systems
  • Stochastic systems

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering

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