The search functionality is under construction.

The search functionality is under construction.

The least-squares linear filtering and fixed-point smoothing problems of uncertainly observed signals are considered when the signal and the observation additive noise are correlated at any sampling time. Recursive algorithms, based on an innovation approach, are proposed without requiring the knowledge of the state-space model generating the signal, but only the autocovariance and crosscovariance functions of the signal and the observation white noise, as well as the probability that the signal exists in the observations.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E91-A No.7 pp.1706-1712

- Publication Date
- 2008/07/01

- Publicized

- Online ISSN
- 1745-1337

- DOI
- 10.1093/ietfec/e91-a.7.1706

- Type of Manuscript
- PAPER

- Category
- Digital Signal Processing

Seiichi NAKAMORI

Raquel CABALLERO-AGUILA

Aurora HERMOSO-CARAZO

Jose D. JIMENEZ-LOPEZ

Josefa LINARES-PEREZ

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.

Copy

Seiichi NAKAMORI, Raquel CABALLERO-AGUILA, Aurora HERMOSO-CARAZO, Jose D. JIMENEZ-LOPEZ, Josefa LINARES-PEREZ, "Recursive Estimation Algorithm Based on Covariances for Uncertainly Observed Signals Correlated with Noise" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 7, pp. 1706-1712, July 2008, doi: 10.1093/ietfec/e91-a.7.1706.

Abstract: The least-squares linear filtering and fixed-point smoothing problems of uncertainly observed signals are considered when the signal and the observation additive noise are correlated at any sampling time. Recursive algorithms, based on an innovation approach, are proposed without requiring the knowledge of the state-space model generating the signal, but only the autocovariance and crosscovariance functions of the signal and the observation white noise, as well as the probability that the signal exists in the observations.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.7.1706/_p

Copy

@ARTICLE{e91-a_7_1706,

author={Seiichi NAKAMORI, Raquel CABALLERO-AGUILA, Aurora HERMOSO-CARAZO, Jose D. JIMENEZ-LOPEZ, Josefa LINARES-PEREZ, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={Recursive Estimation Algorithm Based on Covariances for Uncertainly Observed Signals Correlated with Noise},

year={2008},

volume={E91-A},

number={7},

pages={1706-1712},

abstract={The least-squares linear filtering and fixed-point smoothing problems of uncertainly observed signals are considered when the signal and the observation additive noise are correlated at any sampling time. Recursive algorithms, based on an innovation approach, are proposed without requiring the knowledge of the state-space model generating the signal, but only the autocovariance and crosscovariance functions of the signal and the observation white noise, as well as the probability that the signal exists in the observations.},

keywords={},

doi={10.1093/ietfec/e91-a.7.1706},

ISSN={1745-1337},

month={July},}

Copy

TY - JOUR

TI - Recursive Estimation Algorithm Based on Covariances for Uncertainly Observed Signals Correlated with Noise

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 1706

EP - 1712

AU - Seiichi NAKAMORI

AU - Raquel CABALLERO-AGUILA

AU - Aurora HERMOSO-CARAZO

AU - Jose D. JIMENEZ-LOPEZ

AU - Josefa LINARES-PEREZ

PY - 2008

DO - 10.1093/ietfec/e91-a.7.1706

JO - IEICE TRANSACTIONS on Fundamentals

SN - 1745-1337

VL - E91-A

IS - 7

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - July 2008

AB - The least-squares linear filtering and fixed-point smoothing problems of uncertainly observed signals are considered when the signal and the observation additive noise are correlated at any sampling time. Recursive algorithms, based on an innovation approach, are proposed without requiring the knowledge of the state-space model generating the signal, but only the autocovariance and crosscovariance functions of the signal and the observation white noise, as well as the probability that the signal exists in the observations.

ER -