Derivation of linear estimation algorithms from measurements affected by multiplicative and additive noises

DOI10.1016/J.CAM.2010.01.043zbMath1217.93161DBLPjournals/jcam/Garcia-LigeroHLN10OpenAlexW2025639920WikidataQ59552300 ScholiaQ59552300MaRDI QIDQ966092

Josefa Linares-Pérez, Seiichi Nakamori, Aurora Hermoso-Carazo, María Jesús García-Ligero

Publication date: 27 April 2010

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

Full work available at URL: https://doi.org/10.1016/j.cam.2010.01.043

zbMATH Keywords

filtermultiplicative noisefixed-point smoother

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Estimation and detection in stochastic control theory (93E10) Operator-theoretic methods (93B28)

Related Items (2)

Optimal recursive estimation for networked stochastic uncertain systems with fading measurements and time-correlated channel noises ⋮ Covariance-based estimation from multisensor delayed measurements with random parameter matrices and correlated noises

Cites Work

• Linear recursive discrete-time estimators using covariance information under uncertain observations
• Linear estimation of continuous-discrete linear state space models with multiplicative noise
• On invariant measures of discrete time filters in the correlated signal-noise case
• Filtering of images corrupted by multiplicative and white plus coloured additive noises using covariance information
• Approximate maximum likelihood estimators for array processing in multiplicative noise environments
• Detection of multiplicative noise in stationary random processes using second- and higher order statistics
• A new recursive filter for systems with multiplicative noise
• Robust Kalman filtering for discrete time-varying uncertain systems with multiplicative noises
• Polynomial fixed-point smoothing of uncertainly observed signals based on covariances
• Optimal recursive estimation with uncertain observation
• New recursive estimators from correlated interrupted observations using covariance information

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