Chandrasekhar-type filter for a wide-sense stationary signal from uncertain observations using covariance information

DOI10.1016/S0096-3003(03)00343-6zbMath1058.93055OpenAlexW2152645435WikidataQ59552577 ScholiaQ59552577MaRDI QIDQ1826647

Josefa Linares-Pérez, Aurora Hermoso-Carazo, Seiichi Nakamori, José Domingo Jiménez-López

Publication date: 6 August 2004

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0096-3003(03)00343-6

zbMATH Keywords

linear filteringBernoulli random variablesmultiplicative noisediscrete-time systemChandrasekhar-type algorithmRiccati-type algorithm

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Discrete-time control/observation systems (93C55)

Related Items (4)

Chandrasekhar-type recursive Wiener estimation technique in linear discrete-time stochastic systems ⋮ Different approaches for state filtering in nonlinear systems with uncertain observations ⋮ Computing the Exact Fisher Information Matrix of Periodic State-Space Models ⋮ Extension of the Chandrasekhar filter to the case of periodic state-space models

Cites Work

• Chandrasekhar-type filter using covariance information for white Gaussian plus colored observation noise.
• New design of estimators using covariance information with uncertain observations in linear discrete-time systems
• Multichannel linear and quadratic adaptive filtering based on the Chandrasekhar fast algorithm
• Extended Chandrasekhar recursions
• Linear estimation for discrete-time systems in the presence of time-correlated disturbances and uncertain observations
• Some new algorithms for recursive estimation in constant, linear, discrete-time systems
• Optimal recursive estimation with uncertain observation

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