Blind Robust Estimation with Missing Data for Smart Sensors Using UFIR Filtering

Miguel Vazquez-Olguin, Yuriy S. Shmaliy, Choon Ki Ahn, Oscar G. Ibarra-Manzano

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Smart sensors are often designed to operate under harsh industrial conditions with incomplete information about noise and missing data. Therefore, signal processing algorithms are required to be unbiased, robust, predictive, and desirably blind. In this paper, we propose a novel blind iterative unbiased finite impulse response (UFIR) filtering algorithm, which fits these requirements as a more robust alternative to the Kalman filter (KF). The tradeoff in robustness between the UFIR filter and KF is learned analytically. The predictive UFIR algorithm is developed to operate in control loops under temporary missing data. Experimental verification is given for carbon monoxide concentration and temperature measurements required to monitor urban and industrial environments. High accuracy and precision of the predictive UFIR estimator are demonstrated in a short time and on a long baseline.

Original languageEnglish
Article number7820168
Pages (from-to)1819-1827
Number of pages9
JournalIEEE Sensors Journal
Issue number6
Publication statusPublished - 2017 Mar 15

Bibliographical note

Publisher Copyright:
© 2017 IEEE.


  • Kalman filter
  • Smart sensor
  • blind estimation
  • missing data
  • predictive filtering
  • robustness
  • unbiased FIR filter

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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