Observer-type Kalman innovation filter for uncertain linear systems

Shu Mei Guo, Leang S. Shieh, Guanrong Chen, Norman P. Coleman

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

An observer-type of Kalman innovation filtering algorithm to find a practically implementable "best" Kalman filter, and such an algorithm based on the evolutionary programming (EP) optima-search technique, are proposed, for linear discrete-time systems with time-invariant unknown-but-bounded plant and noise uncertainties. The worst-case parameter set from the stochastic uncertain system represented by the Interval form with respect to the implemented "best" filter is also found In this work for demonstrating the effectiveness of the proposed filtering scheme. The new EP-based algorithm utilizes the global optima-searching capability of EP to find the optimal Kalman filter and state estimates at every iteration, which include both the best possible worst case Interval and the optimal nominal trajectory of the Kalman filtering estimates of the system state vectors. Simulation results are included to show that the new algorithm yields more accurate estimates and is less conservative as compared with other related robust faltering schemes.

Original languageEnglish
Pages (from-to)1406-1418
Number of pages13
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume37
Issue number4
DOIs
Publication statusPublished - 2001 Oct

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering

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