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Author Nakamori, S. ♦ Hermoso-Carazo, A. ♦ Jimenez-Lopez, J. ♦ Linares-Perez, J.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2004
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Filtering algorithms ♦ Riccati equations ♦ Signal processing algorithms ♦ Maximum likelihood detection ♦ Uncertainty ♦ Differential equations ♦ Numerical simulation ♦ Signal analysis ♦ Communication systems ♦ Random variables
Abstract We analyze the least mean-squared error linear filtering problem of a continuous-time wide-sense stationary scalar signal from noisy observations which, in a random way, can consist of signal plus noise or only noise. We assume that the signal is a linear function of the components of the state-vector, and only the system matrix in the state-space model and the crosscovariance function of the state and signal are known. Under the hypothesis that the Bernoulli variables modelling the uncertainty in the observations are independent, with known constant probability of each observation contains signal, we obtain two filtering algorithms to solve this problem: one of them is based on Chandrasekhar-type differential equations and, the other, on Riccati-type ones. The comparison of both algorithms shows that the Chandrasekhar-type one is computationally better than the Riccati-type one. The theoretical results are illustrated by a numerical simulation example.
Description Author affiliation: Dept. of Technol., Kagoshima Univ., Japan (Nakamori, S.)
ISBN 0780385454
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2004-07-18
Publisher Place Spain
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 607.23 kB
Page Count 4
Starting Page 456
Ending Page 459


Source: IEEE Xplore Digital Library