A reorganized innovation approach to linear estimation

Huanshui Zhang, Lihua Xie, Dapeng Zhang, Yeng Chai Soh

Research output: Journal article publicationJournal articleAcademic researchpeer-review

105 Citations (Scopus)

Abstract

This note will address a linear minimum variance estimation of discrete-time systems with instantaneous and delayed measurements. Although the problem may be approached via system augmentation and standard Kalman filtering, the computation of filter may be expensive when the dimension of the system is high and the measurement lag is significant. In this note, a new tool, termed as reorganized innovation sequence, is presented for deriving the optimal filter. The optimal filter is given by two Riccati difference equations (RDEs) with the same dimension as that of the original system. The approach is shown to induce saving of computational cost as compared to the system augmentation approach, especially when the delay is large. Further, it can be extended to solving the more complicated H∞fixed-lag smoothing in Krein space.
Original languageEnglish
Pages (from-to)1810-1814
Number of pages5
JournalIEEE Transactions on Automatic Control
Volume49
Issue number10
DOIs
Publication statusPublished - 1 Oct 2004

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this