Novel cubature Kalman filtering for systems involving nonlinear states and linear measurements

Shiyuan Wang, Jiuchao Feng, Chi Kong Tse

Research output: Journal article publicationJournal articleAcademic researchpeer-review


This paper extends the cubature Kalman filter (CKF) to deal with systems involving nonlinear states and linear measurements (herein called the nonlinear-linear combined systems) with additive noise. The method is referred to as the nonlinear-linear square-root cubature Kalman filtering (NL-SCKF). In NL-SCKF, the cubature rule, combined with a QR decomposition, singular value decomposition and a linear update without requirement of cubature points, is designed to update nonlinear states and linear measurements. In addition, the convergence analysis of NL-SCKF is performed. Simulation results in two selected problems, namely filtering chaotic signals and chaos-based communications, indicate that the proposed NL-SCKF with lower computation complexity achieves the same accuracy as the standard SCKF, and outperforms CKF significantly.
Original languageEnglish
Pages (from-to)314-320
Number of pages7
JournalAEU - International Journal of Electronics and Communications
Issue number1
Publication statusPublished - 1 Jan 2014


  • Computational complexity
  • Convergence analysis
  • Cubature rules
  • Kalman filter
  • Matrix decompositions

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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