Fixed-interval smoother from randomly delayed observations

Author

Nakamori, S. ; Hermoso-Carazo, A. ; Linares-Perez, J. ; Sánchez-Rodríguez, M.I.

Author_Institution

Dept. of Technol., Kagoshima Univ., Japan

fYear

2004

fDate

18-21 July 2004

Firstpage

451

Lastpage

455

Abstract

This paper presents a recursive algorithm for the least-squares linear fixed-interval smoothing problem of discrete-time signals using randomly delayed measurements perturbed by an additive white noise. It is assumed that the autocovariance function of the signal is expressed in a semi-degenerate kernel form and the delay is modelled by a sequence of independent Bernoulli random variables, which indicate if the measurements are up-to-date or delayed by one sampling time. The estimators do not use the state-space model of the signal but only the covariance information about the signal and the additive noise in the observations and the delay probabilities.

Keywords

AWGN; covariance analysis; delays; discrete time filters; least squares approximations; probability; recursive estimation; signal sampling; smoothing methods; additive white noise; autocovariance function; covariance information; delay probability; discrete-time signal; fixed-interval smoother; independent Bernoulli random variable; least-squares linear smoothing; random delayed observation; recursive algorithm; sampling time; semidegenerate kernel form; Additive white noise; Delay effects; Delay estimation; Kernel; Noise measurement; Random variables; Sampling methods; Smoothing methods; State estimation; Time measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2004

Print_ISBN

0-7803-8545-4

Type

conf

DOI

10.1109/SAM.2004.1502988

Filename

1502988