Unbiased FIR Filtering with Incomplete Measurement Information

Dong Ki Ryu; Chang Joo Lee; Sang Kyoo Park; Myo Taeg Lim

doi:10.1007/s12555-018-0316-2

Unbiased FIR Filtering with Incomplete Measurement Information

Dong Ki Ryu, Chang Joo Lee, Sang Kyoo Park, Myo Taeg Lim

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

This paper proposes an unbiased filter with finite impulse response (FIR) structure for linear discrete time systems in state space form with incomplete measurement information. The measurements are transmitted from the plant to the FIR filter imperfectly due to random packet loss or sensor faults. The Bernoulli random process is used to describe the missing measurement details, and the missing data is replaced with recently transmitted data on the missing horizon. The missing horizon can hold the assumption for finite measurement of the FIR filter. Two examples are provided to demonstrate the proposed unbiased FIR (UFIR) filter robustness against temporary model uncertainty and consecutive missing measurement data compared with existing filters considering missing measurement.

Original language	English
Pages (from-to)	330-338
Number of pages	9
Journal	International Journal of Control, Automation and Systems
Volume	18
Issue number	2
DOIs	https://doi.org/10.1007/s12555-018-0316-2
Publication status	Published - 2020 Feb 1

Bibliographical note

Funding Information:
Recommended by Associate Editor Ding Zhai under the direction of Editor Guang-Hong Yang. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2016R1D1A1B01016071).

Publisher Copyright:
© 2020, ICROS, KIEE and Springer.

Keywords

Bernoulli random process
finite impulse response filter
incomplete measurement information
missing horizon
unbiased filtering

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications

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10.1007/s12555-018-0316-2

Cite this

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abstract = "This paper proposes an unbiased filter with finite impulse response (FIR) structure for linear discrete time systems in state space form with incomplete measurement information. The measurements are transmitted from the plant to the FIR filter imperfectly due to random packet loss or sensor faults. The Bernoulli random process is used to describe the missing measurement details, and the missing data is replaced with recently transmitted data on the missing horizon. The missing horizon can hold the assumption for finite measurement of the FIR filter. Two examples are provided to demonstrate the proposed unbiased FIR (UFIR) filter robustness against temporary model uncertainty and consecutive missing measurement data compared with existing filters considering missing measurement.",

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note = "Funding Information: Recommended by Associate Editor Ding Zhai under the direction of Editor Guang-Hong Yang. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2016R1D1A1B01016071). Publisher Copyright: {\textcopyright} 2020, ICROS, KIEE and Springer.",

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N1 - Funding Information: Recommended by Associate Editor Ding Zhai under the direction of Editor Guang-Hong Yang. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2016R1D1A1B01016071). Publisher Copyright: © 2020, ICROS, KIEE and Springer.

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N2 - This paper proposes an unbiased filter with finite impulse response (FIR) structure for linear discrete time systems in state space form with incomplete measurement information. The measurements are transmitted from the plant to the FIR filter imperfectly due to random packet loss or sensor faults. The Bernoulli random process is used to describe the missing measurement details, and the missing data is replaced with recently transmitted data on the missing horizon. The missing horizon can hold the assumption for finite measurement of the FIR filter. Two examples are provided to demonstrate the proposed unbiased FIR (UFIR) filter robustness against temporary model uncertainty and consecutive missing measurement data compared with existing filters considering missing measurement.

AB - This paper proposes an unbiased filter with finite impulse response (FIR) structure for linear discrete time systems in state space form with incomplete measurement information. The measurements are transmitted from the plant to the FIR filter imperfectly due to random packet loss or sensor faults. The Bernoulli random process is used to describe the missing measurement details, and the missing data is replaced with recently transmitted data on the missing horizon. The missing horizon can hold the assumption for finite measurement of the FIR filter. Two examples are provided to demonstrate the proposed unbiased FIR (UFIR) filter robustness against temporary model uncertainty and consecutive missing measurement data compared with existing filters considering missing measurement.

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KW - incomplete measurement information

KW - missing horizon

KW - unbiased filtering

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Unbiased FIR Filtering with Incomplete Measurement Information

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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