Fixed-Lag Smoothing Algorithm under Non-independent Uncertainty(Digital Signal Processing)

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概要

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• This paper discusses the least-squares linear filtering and fixed-lag smoothing problems of discrete-time signals from uncertain observations when the random interruptions in the observation process are modelled by a sequence of not necessarily independent Bernoulli variables. It is assumed that the observations are perturbed by white noise and the autocovariance function of the signal is factorizable. Using an innovation approach we obtain the filtering and fixed-lag smoothing recursive algorithms, which do not require the knowledge of the state-space model generating the signal. Besides the observed values, they use only the matrix functions defining the factorizable autocovariance function of the signal, the noise autocovariance function, the marginal probabilities and the (2, 2)-element of the conditional probability matrices of the Bernoulli variables. The algorithms are applied to estimate a scalar signal which may be transmitted through one of two channels.

• 社団法人電子情報通信学会の論文
• 2005-04-01

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

• 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

• Nakamori S

  The Faculty Of Education Kagoshima University

関連論文

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• 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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