Chandrasekhar-type filter for a wide-sense stationary signal from uncertain observations using covariance information
type: Item
instance of: scholarly article
title: Chandrasekhar-type filter for a wide-sense stationary signal from uncertain observations using covariance information
zbMATH Open document ID: 1058.93055
DOI: 10.1016/S0096-3003(03)00343-6
published in: Applied Mathematics and Computation
publication date: 2004-08-06T00:00:00Z
review text: The authors consider the problem of estimating a scalar signal process \(z(k)\) from an observation process \(y(k)\) related with the signal by the observation equation \[ y(k)= u(k)z(k)+ v(k) \] perturbed by a multiplicative noise \(u(k)\) and by an additive noise \(v(k)\). It is assumed that \(u(k)\) is a sequence of independent Bernoulli random variables with \(P[u(k)= 1]= p\), \(P[u(k)= 0] = 1- p\), i.e., \(u(k)\) determines the presence or absence of the signal in the observations. The authors construct a Chandrasekhar-type recursive algorithm for the linear filtering estimator and compare it with a Riccati-type algorithm.
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 93C55
zbMATH DE Number: 2081691
zbMATH Keywords: discrete-time system
zbMATH Keywords: linear filtering
zbMATH Keywords: Chandrasekhar-type algorithm
zbMATH Keywords: Riccati-type algorithm
zbMATH Keywords: multiplicative noise
zbMATH Keywords: Bernoulli random variables
author: Seiichi Nakamori
author: Aurora Hermoso-Carazo
author: José Domingo Jiménez-López
author: Josefa Linares-Pérez
reviewed by: Grigori N. Milstein
MaRDI profile type: Publication
Wikidata QID: Q59552577
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cites work: Multichannel linear and quadratic adaptive filtering based on the Chandrasekhar fast algorithm.
cites work: Chandrasekhar-type filter using covariance information for white Gaussian plus colored observation noise.
full work available at URL: s0096-3003(03)00343-6
OpenAlex ID: W2152645435
description: scientific article; zbMATH DE number 2081691