# Quadratic estimation from non-independent uncertain observations with coloured noise.

S. Nakamori; R. Caballero; A. Hermoso; J. Jiménez; J. Linares

Extracta Mathematicae (2004)

- Volume: 19, Issue: 3, page 399-413
- ISSN: 0213-8743

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topNakamori, S., et al. "Quadratic estimation from non-independent uncertain observations with coloured noise.." Extracta Mathematicae 19.3 (2004): 399-413. <http://eudml.org/doc/38754>.

@article{Nakamori2004,

abstract = {Recursive least-squares quadratic filtering and fixed-point smoothing algorithms for signal estimation from uncertain observations are derived when the uncertainty is modeled by not necessarily independent variables and the observations contain white plus coloured noise. The proposed estimators do not require the knowledge of the state-space of the model generating the signal, but only the moments, up to the fourth one, of the processes involved, along with the probability that the signal exists in the obervations and the (2,2)-element of the conditional probability matrix of the sequence describing the uncertainty.},

author = {Nakamori, S., Caballero, R., Hermoso, A., Jiménez, J., Linares, J.},

journal = {Extracta Mathematicae},

keywords = {Procesos estocásticos; Procesamiento de señal; Filtrado; Estimación},

language = {eng},

number = {3},

pages = {399-413},

title = {Quadratic estimation from non-independent uncertain observations with coloured noise.},

url = {http://eudml.org/doc/38754},

volume = {19},

year = {2004},

}

TY - JOUR

AU - Nakamori, S.

AU - Caballero, R.

AU - Hermoso, A.

AU - Jiménez, J.

AU - Linares, J.

TI - Quadratic estimation from non-independent uncertain observations with coloured noise.

JO - Extracta Mathematicae

PY - 2004

VL - 19

IS - 3

SP - 399

EP - 413

AB - Recursive least-squares quadratic filtering and fixed-point smoothing algorithms for signal estimation from uncertain observations are derived when the uncertainty is modeled by not necessarily independent variables and the observations contain white plus coloured noise. The proposed estimators do not require the knowledge of the state-space of the model generating the signal, but only the moments, up to the fourth one, of the processes involved, along with the probability that the signal exists in the obervations and the (2,2)-element of the conditional probability matrix of the sequence describing the uncertainty.

LA - eng

KW - Procesos estocásticos; Procesamiento de señal; Filtrado; Estimación

UR - http://eudml.org/doc/38754

ER -

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