FIR Smoothing of Discrete-Time Polynomial Signals in State Space

Shmaliy, Yuriy S.; Morales-Mendoza, Luis J.

doi:10.1109/tsp.2010.2041595

Cited by 58 publications

(66 citation statements)

References 25 publications

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“…Using finite measurements ( ) on the most recent window [ − , ], FMS smoothers [6][7][8][9][10] as well as FMS filters [12][13][14][15] have been developed.…”

Section: Finite Memory Structure Estimation For State-space Modelmentioning

confidence: 99%

“…As shown in [6][7][8][9][10], the fixedlagged system state means there is a fixed delay between the measurement and the availability of its estimate. The delay length is the positive integer with 0 ≤ < .…”

Section: Alternative Fms Smoother With Forgetting Factormentioning

confidence: 99%

“…Recently, to overcome the resulting problems of existing fixed-lag Kalman smoother with the infinite memory structure (IMS) in [1][2][3][4][5], the fixed-lag smoother with the finite memory structure (FMS) has been developed for state estimation in discrete time-invariant systems [6][7][8][9] and discrete time-varying systems [10]. This FMS smoother has been known to have some good properties such as unbiasedness and deadbeat, which cannot be obtained by the IMS smoother.…”

Section: Introductionmentioning

confidence: 99%

“…However, existing FMS smoothers in [6][7][8][9][10] have some limitations. Firstly, information about noise covariances to obtain the optimal estimate should be also assumed to be exactly known as the IMS smoother, which may be somewhat restrictive.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed FMS smoother is represented in a recursive form as well as a matrix form. It is noted that the recursive form has not been developed by the existing FMS smoother in [6][7][8][9][10]. The forgetting factor is shown to be considered as useful parameter to make the estimation performance of the proposed FMS smoother as good as possible.…”

Section: Introductionmentioning

confidence: 99%

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A Finite Memory Structure Smoother with Recursive Form Using Forgetting Factor

Kim

2017

Mathematical Problems in Engineering

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This paper proposes an alternative finite memory structure (FMS) smoother with a recursive form under a least squares criterion using a forgetting factor strategy. The proposed FMS smoother does not require information of the noise covariances as well as the initial state. The proposed FMS smoother is shown to have good inherent properties such as time-invariance, unbiasedness, and deadbeat. The forgetting factor is shown to be considered as useful parameter to make the estimation performance of the proposed FMS smoother as good as possible. Through computer simulations for the F-404 engine system, it is shown that the proposed FMS smoother can be better than the existing FMS smoother for incorrect noise covariances and the IMS smoother for temporary uncertainties.

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“…Using finite measurements ( ) on the most recent window [ − , ], FMS smoothers [6][7][8][9][10] as well as FMS filters [12][13][14][15] have been developed.…”

Section: Finite Memory Structure Estimation For State-space Modelmentioning

confidence: 99%

Section: Alternative Fms Smoother With Forgetting Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Finite Memory Structure Smoother with Recursive Form Using Forgetting Factor

Kim

2017

Mathematical Problems in Engineering

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References

2022

Optimal and Robust State Estimation

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Adaptive control and signal processing literature survey (No. 19)

2010

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk ADAPTIVE SYSTEMSAhn H-S, Chen Y. Periodic adaptive learning compensation of state-dependent disturbance. IET Control Theory and Applications 2010; 4(4):529-538. Angelov P, Kordon A. Adaptive inferential sensors based on evolving fuzzy models. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetic 2010; 40(2):529-539. Bechlioulis C, Rovithakis G. Prescribed performance adaptive control for multi-input multi-output affine in the control nonlinear systems. IEEE Transactions on Automatic Control 2010; 55(5):1220-1226. Bekiaris-Liberis N, Krstic M. Delay-adaptive feedback for linear feedforward systems. Systems and Control Letters 2010; 59(5):277-283. Chen M, Ge S, How B. Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities. IEEE Transactions on Neural Networks 2010; 21(5):796-812. Choi K, Yoo S, Park J, Choi Y. Adaptive formation control in absence of leader's velocity information. IET Control Theory and Applications 2010; 4(4):521-528. Fuentes D, Feng Y, Elliott A, Shetty A, McNichols R, Oden J, Stafford R. Adaptive real-time bioheat transfer models for computer-driven MR-guided laser induced thermal therapy. IEEE Transactions on Biomedical Engineering 2010; 57(5):1024-1030. Hu C, Yao B, Wang Q. Integrated direct/indirect adaptive robust contouring control of a biaxial gantry with accurate parameter estimations. Automatica 2010; 46(4):701-707. Jiménez-Fabián R, Alvarez-Icaza L. An adaptive observer for a shear building with an energy-dissipation device. Control Engineering Practice 2010; 18(4):331-338. Kaylani A, Georgiopoulos M, Mollaghasemi M, Anagnostopoulos G, Sentelle C, Zhong M. An adaptive multiobjective approach to evolving ART architectures. IEEE Transactions on Neural Networks 2010; 21(4):529-550. Lin W, Ma H. Synchronization between adaptively coupled systems with discrete and distributed time-delays. IEEE Transactions on Automatic Control 2010; 55(4): 819-830. Ma L, Cao C, Hovakimyan N, Dobrokhodov V, Kaminer I. Adaptive vision-based guidance law with guaranteed performance bounds. Journal of Guidance, Control, and Dynamics 2010; 33(3):834-852. Olm J, Ramos G, Costa-Castelló R. Adaptive compensation strategy for the tracking/rejection of signals with time-varying frequency in digital repetitive control systems. Journal of Process Control 2010; 20(4):551-558. Peng S, Hwang W-L. Null space pursuit: an operator-based approach to adaptive signal separation. IEEE Transact...

show abstract

FIR Smoothing of Discrete-Time Polynomial Signals in State Space

Cited by 58 publications

References 25 publications

A Finite Memory Structure Smoother with Recursive Form Using Forgetting Factor

A Finite Memory Structure Smoother with Recursive Form Using Forgetting Factor

References

Adaptive control and signal processing literature survey (No. 19)

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