Suboptimal Adaptive Filter for Discrete-Time Linear Stochastic Systems
Summary :
This paper considers the problem of recursive filtering for linear discrete-time systems with uncertain observation. A new approximate adaptive filter with a parallel structure is herein proposed. It is based on the optimal mean square combination of arbitrary number of correlated estimates which is also derived. The equation for error covariance characterizing the mean-square accuracy of the new filter is derived. In consequence of parallel structure of the filtering equations the parallel computers can be used for their design. It is shown that this filter is very effective for multisensor systems containing different types of sensors. A practical implementation issue to consider this filter is also addressed. Example demonstrates the accuracy and efficiency of the proposed filter.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.3 pp.620-625
- Publication Date
- 2005/03/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietfec/e88-a.3.620
- Type of Manuscript
- Special Section PAPER (Special Section on Adaptive Signal Processing and Its Applications)
- Category
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Daebum CHOI, Vladimir SHIN, Jun IL AHN, Byung-Ha AHN, "Suboptimal Adaptive Filter for Discrete-Time Linear Stochastic Systems" in IEICE TRANSACTIONS on Fundamentals,
vol. E88-A, no. 3, pp. 620-625, March 2005, doi: 10.1093/ietfec/e88-a.3.620.
Abstract: This paper considers the problem of recursive filtering for linear discrete-time systems with uncertain observation. A new approximate adaptive filter with a parallel structure is herein proposed. It is based on the optimal mean square combination of arbitrary number of correlated estimates which is also derived. The equation for error covariance characterizing the mean-square accuracy of the new filter is derived. In consequence of parallel structure of the filtering equations the parallel computers can be used for their design. It is shown that this filter is very effective for multisensor systems containing different types of sensors. A practical implementation issue to consider this filter is also addressed. Example demonstrates the accuracy and efficiency of the proposed filter.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.3.620/_p
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@ARTICLE{e88-a_3_620,
author={Daebum CHOI, Vladimir SHIN, Jun IL AHN, Byung-Ha AHN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Suboptimal Adaptive Filter for Discrete-Time Linear Stochastic Systems},
year={2005},
volume={E88-A},
number={3},
pages={620-625},
abstract={This paper considers the problem of recursive filtering for linear discrete-time systems with uncertain observation. A new approximate adaptive filter with a parallel structure is herein proposed. It is based on the optimal mean square combination of arbitrary number of correlated estimates which is also derived. The equation for error covariance characterizing the mean-square accuracy of the new filter is derived. In consequence of parallel structure of the filtering equations the parallel computers can be used for their design. It is shown that this filter is very effective for multisensor systems containing different types of sensors. A practical implementation issue to consider this filter is also addressed. Example demonstrates the accuracy and efficiency of the proposed filter.},
keywords={},
doi={10.1093/ietfec/e88-a.3.620},
ISSN={},
month={March},}
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TY - JOUR
TI - Suboptimal Adaptive Filter for Discrete-Time Linear Stochastic Systems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 620
EP - 625
AU - Daebum CHOI
AU - Vladimir SHIN
AU - Jun IL AHN
AU - Byung-Ha AHN
PY - 2005
DO - 10.1093/ietfec/e88-a.3.620
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2005
AB - This paper considers the problem of recursive filtering for linear discrete-time systems with uncertain observation. A new approximate adaptive filter with a parallel structure is herein proposed. It is based on the optimal mean square combination of arbitrary number of correlated estimates which is also derived. The equation for error covariance characterizing the mean-square accuracy of the new filter is derived. In consequence of parallel structure of the filtering equations the parallel computers can be used for their design. It is shown that this filter is very effective for multisensor systems containing different types of sensors. A practical implementation issue to consider this filter is also addressed. Example demonstrates the accuracy and efficiency of the proposed filter.
ER -
English
