A Finite Memory Structure Smoothing Filter and Its Equivalent Relationship with Existing Filters


KIPS Transactions on Computer and Communication Systems, Vol. 10, No. 2, pp. 53-58, Feb. 2021
https://doi.org/10.3745/KTCCS.2021.10.2.53,   PDF Download:
Keywords: Finite Memory Structure, Smoothing Filter, Kalman filter, robustness, Unbiasedness
Abstract

In this paper, an alternative finite memory structure(FMS) smoothing filter is developed for discrete-time state-space model with a control input. To obtain the FMS smoothing filter, unbiasedness will be required beforehand in addition to a performance criteria of minimum variance. The FMS smoothing filter is obtained by directly solving an optimization problem with the unbiasedness constraint using only finite measurements and inputs on the most recent window. The proposed FMS smoothing filter is shown to have intrinsic good properties such as deadbeat and time-invariance. In addition, the proposed FMS smoothing filter is shown to be equivalent to existing FMS filters according to the delay length between the measurement and the availability of its estimate. Finally, to verify intrinsic robustness of the proposed FMS smoothing filter, computer simulations are performed for a temporary model uncertainty. Simulation results show that the proposed FMS smoothing filter can be better than the standard FMS filter and Kalman filter.


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Cite this article
[IEEE Style]
M. H. Kim and P. S. Kim, "A Finite Memory Structure Smoothing Filter and Its Equivalent Relationship with Existing Filters," KIPS Transactions on Computer and Communication Systems, vol. 10, no. 2, pp. 53-58, 2021. DOI: https://doi.org/10.3745/KTCCS.2021.10.2.53.

[ACM Style]
Min Hui Kim and Pyung Soo Kim. 2021. A Finite Memory Structure Smoothing Filter and Its Equivalent Relationship with Existing Filters. KIPS Transactions on Computer and Communication Systems, 10, 2, (2021), 53-58. DOI: https://doi.org/10.3745/KTCCS.2021.10.2.53.