New receding horizon fir estimator for blind smart sensing of velocity via position measurements

Choon Ki Ahn, Yuriy S. Shmaliy

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Smart sensors often require that embedded estimators are robust and blind for given averaging horizons. This brief proposes a new receding horizon (RH) finite impulse response (FIR) velocity estimator that fits these needs by utilizing data from N recent discrete position measurements with fading weights. The conventional Kalman estimator typically exhibits poor performance and may even diverge under imprecisely defined noise statistics and/or numerical errors. In contrast, the proposed weighted RH FIR estimator does not require any information about noise, which makes it more robust and blind for a given N. The weighted RH FIR estimator minimizes the effects of uncertainties caused by imprecisely defined noise statistics and/or numerical errors and demonstrates better robustness than the existing FIR estimators. We also discuss how to choose the optimal horizon size for the weighted RH FIR estimator. The better performance of the proposed weighted RH FIR estimator against the Kalman and FIR estimators is shown through simulations under diverse operation conditions.

Original languageEnglish
Pages (from-to)135-139
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume65
Issue number1
DOIs
Publication statusPublished - 2018 Jan 1

Keywords

  • Blind operation
  • Fading weight
  • Kalman estimator
  • Receding horizon
  • Robustness
  • Velocity estimation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'New receding horizon fir estimator for blind smart sensing of velocity via position measurements'. Together they form a unique fingerprint.

Cite this