Linear and quadratic least-squares estimation using measurements with correlated one-step random delay

Authors:
A. Hermoso-Carazo;J. Linares-Pérez
Affiliations:
Departamento de Estadística e I.O., Universidad de Granada, Campus Fuentenueva, s/n, 18071 Granada, Spain;Departamento de Estadística e I.O., Universidad de Granada, Campus Fuentenueva, s/n, 18071 Granada, Spain
Venue:
Digital Signal Processing
Year:
2008

Citing 6
Cited 2

Polynomial Filtering for Linear Discrete Time Non-Gaussian Systems

SIAM Journal on Control and Optimization
Recursive estimator for linear and nonlinear systems with uncertain observations

Signal Processing
Stochastic analysis and control of real-time systems with random time delays

Automatica (Journal of IFAC)
Quadratic Estimation of Multivariate Signals from Randomly Delayed Measurements*

Multidimensional Systems and Signal Processing
Least-Squares Linear Smoothers from Randomly Delayed Observations with Correlation in the Delay

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Hidden Markov model state estimation with randomly delayedobservations

IEEE Transactions on Signal Processing

Recursive least-squares quadratic smoothing from measurements with packet dropouts

Signal Processing
Least-squares linear estimators using measurements transmitted by different sensors with packet dropouts

Digital Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper considers the linear and quadratic least-squares estimation of a discrete-time signal from observations randomly delayed by one sampling time such that the delay at a given time instant depends on a previous delay. It is assumed that the signal is measured with an additive white noise and that the delay in the observations is characterized by a set of Bernoulli variables which are correlated when the difference between times is equal to a certain value m. Linear and quadratic recursive filtering and fixed-point smoothing algorithms for such a class of models are constructed using an innovation approach; they do not require full knowledge of the state-space model for the signal process, but just the moments up to the fourth order of the signal (admitting a separable form) and the observation noise, as well as the probability and correlation of the Bernoulli variables modelling the delay. Recursive expressions for the estimation error covariance matrices are also given, and the performance of the different estimators is illustrated by means of a numerical example.