IEICE TRANSACTIONS on Fundamentals
Fixed-Point, Fixed-Interval and Fixed-Lag Smoothing Algorithms from Uncertain Observations Based on Covariances
Summary :
This paper treats the least-squares linear filtering and smoothing problems of discrete-time signals from uncertain observations when the random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables. Using an innovation approach we obtain the filtering algorithm and a general expression for the smoother which leads to fixed-point, fixed-interval and fixed-lag smoothing recursive algorithms. The proposed algorithms do not require the knowledge of the state-space model generating the signal, but only the covariance information of the signal and the observation noise, as well as the probability that the signal exists in the observed values.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.12 pp.3350-3359
- Publication Date
- 2004/12/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
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Seiichi NAKAMORI, Raquel CABALLERO-AGUILA, Aurora HERMOSO-CARAZO, Josefa LINARES-PEREZ, "Fixed-Point, Fixed-Interval and Fixed-Lag Smoothing Algorithms from Uncertain Observations Based on Covariances" in IEICE TRANSACTIONS on Fundamentals,
vol. E87-A, no. 12, pp. 3350-3359, December 2004, doi: .
Abstract: This paper treats the least-squares linear filtering and smoothing problems of discrete-time signals from uncertain observations when the random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables. Using an innovation approach we obtain the filtering algorithm and a general expression for the smoother which leads to fixed-point, fixed-interval and fixed-lag smoothing recursive algorithms. The proposed algorithms do not require the knowledge of the state-space model generating the signal, but only the covariance information of the signal and the observation noise, as well as the probability that the signal exists in the observed values.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e87-a_12_3350/_p
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@ARTICLE{e87-a_12_3350,
author={Seiichi NAKAMORI, Raquel CABALLERO-AGUILA, Aurora HERMOSO-CARAZO, Josefa LINARES-PEREZ, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fixed-Point, Fixed-Interval and Fixed-Lag Smoothing Algorithms from Uncertain Observations Based on Covariances},
year={2004},
volume={E87-A},
number={12},
pages={3350-3359},
abstract={This paper treats the least-squares linear filtering and smoothing problems of discrete-time signals from uncertain observations when the random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables. Using an innovation approach we obtain the filtering algorithm and a general expression for the smoother which leads to fixed-point, fixed-interval and fixed-lag smoothing recursive algorithms. The proposed algorithms do not require the knowledge of the state-space model generating the signal, but only the covariance information of the signal and the observation noise, as well as the probability that the signal exists in the observed values.},
keywords={},
doi={},
ISSN={},
month={December},}
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TY - JOUR
TI - Fixed-Point, Fixed-Interval and Fixed-Lag Smoothing Algorithms from Uncertain Observations Based on Covariances
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3350
EP - 3359
AU - Seiichi NAKAMORI
AU - Raquel CABALLERO-AGUILA
AU - Aurora HERMOSO-CARAZO
AU - Josefa LINARES-PEREZ
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2004
AB - This paper treats the least-squares linear filtering and smoothing problems of discrete-time signals from uncertain observations when the random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables. Using an innovation approach we obtain the filtering algorithm and a general expression for the smoother which leads to fixed-point, fixed-interval and fixed-lag smoothing recursive algorithms. The proposed algorithms do not require the knowledge of the state-space model generating the signal, but only the covariance information of the signal and the observation noise, as well as the probability that the signal exists in the observed values.
ER -
English
