IEICE TRANSACTIONS on Fundamentals
Least-Squares Linear Smoothers from Randomly Delayed Observations with Correlation in the Delay
Summary :
This paper discusses the least-squares linear filtering and smoothing (fixed-point and fixed-interval) problems of discrete-time signals from observations, perturbed by additive white noise, which can be randomly delayed by one sampling time. It is assumed that the Bernoulli random variables characterizing delay measurements are correlated in consecutive time instants. The marginal distribution of each of these variables, specified by the probability of a delay in the measurement, as well as their correlation function, are known. Using an innovation approach, the filtering, fixed-point and fixed-interval smoothing recursive algorithms are obtained without requiring the state-space model generating the signal; they use only the covariance functions of the signal and the noise, the delay probabilities and the correlation function of the Bernoulli variables. The algorithms are applied to a particular transmission model with stand-by sensors for the immediate replacement of a failed unit.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E89-A No.2 pp.486-493
- Publication Date
- 2006/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e89-a.2.486
- Type of Manuscript
- PAPER
- 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
Seiichi NAKAMORI, Aurora HERMOSO-CARAZO, Josefa LINARES-PEREZ, "Least-Squares Linear Smoothers from Randomly Delayed Observations with Correlation in the Delay" in IEICE TRANSACTIONS on Fundamentals,
vol. E89-A, no. 2, pp. 486-493, February 2006, doi: 10.1093/ietfec/e89-a.2.486.
Abstract: This paper discusses the least-squares linear filtering and smoothing (fixed-point and fixed-interval) problems of discrete-time signals from observations, perturbed by additive white noise, which can be randomly delayed by one sampling time. It is assumed that the Bernoulli random variables characterizing delay measurements are correlated in consecutive time instants. The marginal distribution of each of these variables, specified by the probability of a delay in the measurement, as well as their correlation function, are known. Using an innovation approach, the filtering, fixed-point and fixed-interval smoothing recursive algorithms are obtained without requiring the state-space model generating the signal; they use only the covariance functions of the signal and the noise, the delay probabilities and the correlation function of the Bernoulli variables. The algorithms are applied to a particular transmission model with stand-by sensors for the immediate replacement of a failed unit.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.2.486/_p
Copy
@ARTICLE{e89-a_2_486,
author={Seiichi NAKAMORI, Aurora HERMOSO-CARAZO, Josefa LINARES-PEREZ, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Least-Squares Linear Smoothers from Randomly Delayed Observations with Correlation in the Delay},
year={2006},
volume={E89-A},
number={2},
pages={486-493},
abstract={This paper discusses the least-squares linear filtering and smoothing (fixed-point and fixed-interval) problems of discrete-time signals from observations, perturbed by additive white noise, which can be randomly delayed by one sampling time. It is assumed that the Bernoulli random variables characterizing delay measurements are correlated in consecutive time instants. The marginal distribution of each of these variables, specified by the probability of a delay in the measurement, as well as their correlation function, are known. Using an innovation approach, the filtering, fixed-point and fixed-interval smoothing recursive algorithms are obtained without requiring the state-space model generating the signal; they use only the covariance functions of the signal and the noise, the delay probabilities and the correlation function of the Bernoulli variables. The algorithms are applied to a particular transmission model with stand-by sensors for the immediate replacement of a failed unit.},
keywords={},
doi={10.1093/ietfec/e89-a.2.486},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - Least-Squares Linear Smoothers from Randomly Delayed Observations with Correlation in the Delay
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 486
EP - 493
AU - Seiichi NAKAMORI
AU - Aurora HERMOSO-CARAZO
AU - Josefa LINARES-PEREZ
PY - 2006
DO - 10.1093/ietfec/e89-a.2.486
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2006
AB - This paper discusses the least-squares linear filtering and smoothing (fixed-point and fixed-interval) problems of discrete-time signals from observations, perturbed by additive white noise, which can be randomly delayed by one sampling time. It is assumed that the Bernoulli random variables characterizing delay measurements are correlated in consecutive time instants. The marginal distribution of each of these variables, specified by the probability of a delay in the measurement, as well as their correlation function, are known. Using an innovation approach, the filtering, fixed-point and fixed-interval smoothing recursive algorithms are obtained without requiring the state-space model generating the signal; they use only the covariance functions of the signal and the noise, the delay probabilities and the correlation function of the Bernoulli variables. The algorithms are applied to a particular transmission model with stand-by sensors for the immediate replacement of a failed unit.
ER -
English
