Extended-Kalman-filter-based chaotic communication

Jason Sheng-Hon Tsai, Jiang Ming Yu, Jose I. Canelon, Leang S. Shieh

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Together with the optimal linearization technique, an extended-Kalman-filter-based chaotic communication is first proposed in this paper. First, the optimal linearization technique is utilized to find the exact linear models of the chaotic system at operating states of interest. Then, an extended Kalman filter (EKF) algorithm is used to estimate both the parameters and states where the message is already embedded. By using the EKF together with the optimal linear model, the message can be recovered well at the receiver's end. Numerical examples and simulations are given to show the effectiveness of the proposed methodology.

Original languageEnglish
Pages (from-to)58-79
Number of pages22
JournalIMA Journal of Mathematical Control and Information
Volume22
Issue number1
DOIs
Publication statusPublished - 2005 Mar 1

Fingerprint

Extended Kalman filters
Kalman Filter
Linearization Techniques
Linearization
Linear Model
Communication
Chaotic systems
Chaotic System
Receiver
Numerical Simulation
Numerical Examples
Methodology
Estimate

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization
  • Applied Mathematics

Cite this

Tsai, Jason Sheng-Hon ; Yu, Jiang Ming ; Canelon, Jose I. ; Shieh, Leang S. / Extended-Kalman-filter-based chaotic communication. In: IMA Journal of Mathematical Control and Information. 2005 ; Vol. 22, No. 1. pp. 58-79.
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Extended-Kalman-filter-based chaotic communication. / Tsai, Jason Sheng-Hon; Yu, Jiang Ming; Canelon, Jose I.; Shieh, Leang S.

In: IMA Journal of Mathematical Control and Information, Vol. 22, No. 1, 01.03.2005, p. 58-79.

Research output: Contribution to journalArticle

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