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.
|Number of pages||9|
|Journal||International Journal of Control, Automation and Systems|
|Publication status||Published - 2020 Feb 1|
Bibliographical noteFunding 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).
© 2020, ICROS, KIEE and Springer.
- 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