New Multi-Step FIR Predictors for State-Space Signal Models
Summary :
In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discrete-time state-space signal models. This predictor is called a maximum likelihood FIR predictor (MLFP). The MLFP is linear with the most recent finite outputs and does not require a prior initial state information on a receding horizon. It is shown that the proposed MLFP possesses the unbiasedness property and the deadbeat property. Simulation study illustrates that the proposed MLFP is more robust against uncertainties and faster in convergence than the conventional multi-step Kalman predictor.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.4 pp.1233-1236
- Publication Date
- 2009/04/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E92.A.1233
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
ChoonKi AHN, "New Multi-Step FIR Predictors for State-Space Signal Models" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 4, pp. 1233-1236, April 2009, doi: 10.1587/transfun.E92.A.1233.
Abstract: In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discrete-time state-space signal models. This predictor is called a maximum likelihood FIR predictor (MLFP). The MLFP is linear with the most recent finite outputs and does not require a prior initial state information on a receding horizon. It is shown that the proposed MLFP possesses the unbiasedness property and the deadbeat property. Simulation study illustrates that the proposed MLFP is more robust against uncertainties and faster in convergence than the conventional multi-step Kalman predictor.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.1233/_p
Copy
@ARTICLE{e92-a_4_1233,
author={ChoonKi AHN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={New Multi-Step FIR Predictors for State-Space Signal Models},
year={2009},
volume={E92-A},
number={4},
pages={1233-1236},
abstract={In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discrete-time state-space signal models. This predictor is called a maximum likelihood FIR predictor (MLFP). The MLFP is linear with the most recent finite outputs and does not require a prior initial state information on a receding horizon. It is shown that the proposed MLFP possesses the unbiasedness property and the deadbeat property. Simulation study illustrates that the proposed MLFP is more robust against uncertainties and faster in convergence than the conventional multi-step Kalman predictor.},
keywords={},
doi={10.1587/transfun.E92.A.1233},
ISSN={1745-1337},
month={April},}
Copy
TY - JOUR
TI - New Multi-Step FIR Predictors for State-Space Signal Models
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1233
EP - 1236
AU - ChoonKi AHN
PY - 2009
DO - 10.1587/transfun.E92.A.1233
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2009
AB - In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discrete-time state-space signal models. This predictor is called a maximum likelihood FIR predictor (MLFP). The MLFP is linear with the most recent finite outputs and does not require a prior initial state information on a receding horizon. It is shown that the proposed MLFP possesses the unbiasedness property and the deadbeat property. Simulation study illustrates that the proposed MLFP is more robust against uncertainties and faster in convergence than the conventional multi-step Kalman predictor.
ER -
English
