Signal estimation with multiple delayed sensors using covariance information

Authors:
R. Caballero-Águila;A. Hermoso-Carazo;J. D. Jiménez-López;J. Linares-Pérez;S. Nakamori
Affiliations:
Departamento de Estadística e I.O., Universidad de Jaén, Paraje Las Lagunillas, s/n, 23071 Jaén, Spain;Departamento de Estadística e I.O., Universidad de Granada, Campus Fuentenueva, s/n, 18071 Granada, Spain;Departamento de Estadística e I.O., Universidad de Jaén, Paraje Las Lagunillas, s/n, 23071 Jaén, Spain;Departamento de Estadística e I.O., Universidad de Granada, Campus Fuentenueva, s/n, 18071 Granada, Spain;Department of Technology, Faculty of Education, Kagoshima University, Kohrimoto, Kagoshima 890-0065, Japan
Venue:
Digital Signal Processing
Year:
2010

Citing 3
Cited 2

Quadratic Estimation of Multivariate Signals from Randomly Delayed Measurements*

Multidimensional Systems and Signal Processing
Minimum variance generalized state estimators for multiple sensors with different delay rates

Signal Processing
Hidden Markov model state estimation with randomly delayedobservations

IEEE Transactions on Signal Processing

Distributed H∞ state estimation with stochastic parameters and nonlinearities through sensor networks: The finite-horizon case

Automatica (Journal of IFAC)
Least-squares linear estimators using measurements transmitted by different sensors with packet dropouts

Digital Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recursive filtering and smoothing algorithms to estimate a signal from noisy measurements coming from multiple randomly delayed sensors, with different delay characteristics, are proposed. To design these algorithms an innovation approach is used, assuming that the state-space model of the signal is unknown and using only covariance information. To measure the precision of the proposed estimators formulas to calculate the filtering and smoothing error covariance matrices are also derived. The effectiveness of the estimators is illustrated by a numerical simulation example where a signal is estimated using observations from two randomly delayed sensors having different delay properties.