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Assumed Model

The Experts below are selected from a list of 261 Experts worldwide ranked by ideXlab platform

Ian R Petersen – 1st expert on this subject based on the ideXlab platform

  • technical communique robust state estimation and Model validation for discrete time uncertain systems with a deterministic description of noise and uncertainty
    Automatica, 1998
    Co-Authors: Andrey V Savkin, Ian R Petersen

    Abstract:

    The paper presents a new approach to robust state estimation for a class of uncertain discrete-time systems with a deterministic description of noise and uncertainty. The main result is a recursive scheme for constructing an ellipsoidal state estimation set of all states consistent with the measured output and the given noise and uncertainty description. The paper also includes a result on Model validation whereby it can be determined if the Assumed Model is consistent with measured data.

  • Robust State Estimation for Discrete-Time Uncertain Systems with a Deterministic Description of Noise and Unqrtainty
    IFAC Proceedings Volumes, 1995
    Co-Authors: Andrey V Savkin, Ian R Petersen

    Abstract:

    Abstract The paper presents a new approach to robust state estimation for a class of uncertain discrete-time systems with a deterministic description of noise and uncertainty. The main result is a recursive scheme for constructing an ellipsoidal state estimation set of all states consistent with the measured output and the given noise and uncertainty description. The paper also includes a result on Model validation whereby it can be determined if the Assumed Model is consistant with measured data.

Andrey V Savkin – 2nd expert on this subject based on the ideXlab platform

  • technical communique robust state estimation and Model validation for discrete time uncertain systems with a deterministic description of noise and uncertainty
    Automatica, 1998
    Co-Authors: Andrey V Savkin, Ian R Petersen

    Abstract:

    The paper presents a new approach to robust state estimation for a class of uncertain discrete-time systems with a deterministic description of noise and uncertainty. The main result is a recursive scheme for constructing an ellipsoidal state estimation set of all states consistent with the measured output and the given noise and uncertainty description. The paper also includes a result on Model validation whereby it can be determined if the Assumed Model is consistent with measured data.

  • Robust State Estimation for Discrete-Time Uncertain Systems with a Deterministic Description of Noise and Unqrtainty
    IFAC Proceedings Volumes, 1995
    Co-Authors: Andrey V Savkin, Ian R Petersen

    Abstract:

    Abstract The paper presents a new approach to robust state estimation for a class of uncertain discrete-time systems with a deterministic description of noise and uncertainty. The main result is a recursive scheme for constructing an ellipsoidal state estimation set of all states consistent with the measured output and the given noise and uncertainty description. The paper also includes a result on Model validation whereby it can be determined if the Assumed Model is consistant with measured data.

Jian Li – 3rd expert on this subject based on the ideXlab platform

  • On the applicability of 2-D damped exponential Models to synthetic aperture radar
    1995 International Conference on Acoustics Speech and Signal Processing, 1995
    Co-Authors: M.p. Pepin, M.p. Clark, Jian Li

    Abstract:

    This paper examines the Modeling of synthetic aperture radar (SAR) phase histories with 2-D damped exponential Models of low order. The use of a low order Model is warranted when the radar returns are attributable to a small number of point scatterers. We show that the fit of the widely used damped exponential Model is highly dependent on the image scene. Specifically, current high resolution methods have limited applicability due to mismatch between the Assumed Model and observed data.

  • ICASSP – On the applicability of 2-D damped exponential Models to synthetic aperture radar
    1995 International Conference on Acoustics Speech and Signal Processing, 1995
    Co-Authors: M.p. Pepin, M.p. Clark, Jian Li

    Abstract:

    This paper examines the Modeling of synthetic aperture radar (SAR) phase histories with 2-D damped exponential Models of low order. The use of a low order Model is warranted when the radar returns are attributable to a small number of point scatterers. We show that the fit of the widely used damped exponential Model is highly dependent on the image scene. Specifically, current high resolution methods have limited applicability due to mismatch between the Assumed Model and observed data.