Low-cost adaptive square-root cubature Kalman filter for systems with process model uncertainty

An Zhang, Shuida Bao, Wenhao Bi, Yuan Yuan

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

A novel low-cost adaptive square-root cubature Kalman filter (LCASCKF) is proposed to enhance the robustness of process models while only increasing the computational load slightly. It is well-known that the Kalman filter cannot handle uncertainties in a process model, such as initial state estimation errors, parameter mismatch and abrupt state changes. These uncertainties severely affect filter performance and may even provoke divergence. A strong tracking filter (STF), which utilizes a suboptimal fading factor, is an adaptive approach that is commonly adopted to solve this problem. However, if the strong tracking SCKF (STSCKF) uses the same method as the extended Kalman filter (EKF) to introduce the suboptimal fading factor, it greatly increases the computational load. To avoid this problem, a low-cost introductory method is proposed and a hypothesis testing theory is applied to detect uncertainties. The computational load analysis is performed by counting the total number of floating-point operations and it is found that the computational load of LCASCKF is close to that of SCKF. Experimental results prove that the LCASCKF performs as well as STSCKF, while the increase in computational load is much lower than STSCKF.

Original languageEnglish
Article number7784176
Pages (from-to)945-953
Number of pages9
JournalJournal of Systems Engineering and Electronics
Volume27
Issue number5
DOIs
StatePublished - Oct 2016

Keywords

  • computational load
  • robustness
  • square-root cubature Kalman filter
  • strong tracking filter

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