Abstract
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 language | English |
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Article number | 7820168 |
Pages (from-to) | 1819-1827 |
Number of pages | 9 |
Journal | IEEE Sensors Journal |
Volume | 17 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2017 Mar 15 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Kalman filter
- Smart sensor
- blind estimation
- missing data
- predictive filtering
- robustness
- unbiased FIR filter
ASJC Scopus subject areas
- Instrumentation
- Electrical and Electronic Engineering