Fixed-Interval Smoothing from Uncertain Observations with White Plus Coloured Noises Using Covariance Information(Digital Signal Processing)

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概要

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• This paper presents recursive algorithms for the least mean-squared error linear filtering and fixed-interval smoothing estimators, from uncertain observations for the case of white and white plus coloured observation noises. The estimators are obtained by an innovation approach and do not use the state-space model, but only covariance information about the signal and the observation noises, as well as the probability that the signal exists in the observed values. Therefore the algorithms are applicable not only to signal processes that can be estimated by the conventional formulation using the state-space model but also to those for which a realization of the state-space model is not available. It is assumed that both the signal and the coloured noise autocovariance functions are expressed in a semi-degenerate kernel form. Since the semi-degenerate kernel is suitable for expressing autocovariance functions of non-stationary or stationary signal processes, the proposed estimators provide estimates of general signal processes.

• 社団法人電子情報通信学会の論文
• 2004-05-01

著者

• Linares-perez Josefa

  The Department Of Statistics Granada University

• Linares-perez Josefa

  Department Of Statistics Granada University

• NAKAMORI Seiichi

  Faculty of Education, Kagoshima University

• CABALLERO-AGUILA Raquel

  Department of Statistics, Jaen University

• HERMOSO-CARAZO Aurora

  Department of Statistics, Granada University

• Nakamori Seiichi

  Faculty Of Education Kagoshima University

• Hermoso-carazo Aurora

  Department Of Statistics Granada University

• Hermoso-carazo Aurora

  Departamento De Estad'istica E Investigaci'on Operativa Facultades De Ciencias Universidad

• Caballero-aguila Raquel

  Department Of Statistics Jaen University

• Nakamori S

  The Faculty Of Education Kagoshima University

関連論文

• Recursive Estimation Algorithm Based on Covariances for Uncertainly Observed Signals Correlated with Noise
• Filtering in Generalized Signal-Dependent Noise Model Using Covariance Information
• Least-Squares Linear Smoothers from Randomly Delayed Observations with Correlation in the Delay (Digital Signal Processing)
• Fixed-Lag Smoothing Algorithm under Non-independent Uncertainty(Digital Signal Processing)
• Fixed-Point, Fixed-Interval and Fixed-Lag Smoothing Algorithms from Uncertain Observations Based on Covariances(Digital Signal Processing)
• Estimation Algorithm from Delayed Measurements with Correlation between Signal and Noise Using Covariance Information(Systems and Control)
• Fixed-Interval Smoothing from Uncertain Observations with White Plus Coloured Noises Using Covariance Information(Digital Signal Processing)
• Second-Order Polynomial Estimators from Non-independent Uncertain Observations Using Covariance Information
• Recursive Estimation Technique of Signal from Output Measurement Data in Linear Discrete-Time Systems
• Design of estimators using covariance information with uncertain observations in linear discrete-time distributed parameter systems
• Chandrasekhar-Type Recursive Wiener Filter Using Covariance Information in Linear Discrete-Time Wide-Sense Stationary Stochastic Systems
• Estimation Technique Using Covariance Data in Linear Continuous Stochastic Systems
• Optimal Filtering Algorithm Using Covariance Information In Linear Continuous Distributed Parameter Systems
• Design of Linear Discrete-Time Stochastic Estimators Using Covariance Information in Krein Spaces
• Design of Recursive Wiener Smoother Given Covariance Information
• Design of Estimators Using Covarianee Information in Discrete-Time Stochastic Systems with Nonlinear Observation Mechanism

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