New multi-step fir predictors for state-space signal models

Research output: Contribution to journalArticle

Abstract

In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discretetime 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.

Original languageEnglish
Pages (from-to)1233-1236
Number of pages4
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE92-A
Issue number4
DOIs
Publication statusPublished - 2009 Dec 1
Externally publishedYes

Fingerprint

Maximum likelihood
Predictors
State Space
Maximum Likelihood
Impulse response
Impulse Response
Model
Unbiasedness
Horizon
Discrete-time
Simulation Study
Uncertainty
Output

Keywords

  • Deadbeat property
  • FIR structure
  • Maximum likelihood
  • Multi-step predictor
  • Unbiasedness property

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

New multi-step fir predictors for state-space signal models. / Ahn, Choon Ki.

In: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E92-A, No. 4, 01.12.2009, p. 1233-1236.

Research output: Contribution to journalArticle

@article{c88077e0dd3448708d317bc9e5d400bc,
title = "New multi-step fir predictors for state-space signal models",
abstract = "In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discretetime 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 = "Deadbeat property, FIR structure, Maximum likelihood, Multi-step predictor, Unbiasedness property",
author = "Ahn, {Choon Ki}",
year = "2009",
month = "12",
day = "1",
doi = "10.1587/transfun.E92.A.1233",
language = "English",
volume = "E92-A",
pages = "1233--1236",
journal = "IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences",
issn = "0916-8508",
publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",
number = "4",

}

TY - JOUR

T1 - New multi-step fir predictors for state-space signal models

AU - Ahn, Choon Ki

PY - 2009/12/1

Y1 - 2009/12/1

N2 - In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discretetime 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.

AB - In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discretetime 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.

KW - Deadbeat property

KW - FIR structure

KW - Maximum likelihood

KW - Multi-step predictor

KW - Unbiasedness property

UR - http://www.scopus.com/inward/record.url?scp=77956049787&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956049787&partnerID=8YFLogxK

U2 - 10.1587/transfun.E92.A.1233

DO - 10.1587/transfun.E92.A.1233

M3 - Article

AN - SCOPUS:77956049787

VL - E92-A

SP - 1233

EP - 1236

JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

SN - 0916-8508

IS - 4

ER -