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Grid-based Nonlinear Estimation and its Applications presents new
Bayesian nonlinear estimation techniques developed in the last two
decades. Grid-based estimation techniques are based on efficient
and precise numerical integration rules to improve performance of
the traditional Kalman filtering based estimation for nonlinear and
uncertainty dynamic systems. The unscented Kalman filter,
Gauss-Hermite quadrature filter, cubature Kalman filter,
sparse-grid quadrature filter, and many other numerical grid-based
filtering techniques have been introduced and compared in this
book. Theoretical analysis and numerical simulations are provided
to show the relationships and distinct features of different
estimation techniques. To assist the exposition of the filtering
concept, preliminary mathematical review is provided. In addition,
rather than merely considering the single sensor estimation,
multiple sensor estimation, including the centralized and
decentralized estimation, is included. Different decentralized
estimation strategies, including consensus, diffusion, and
covariance intersection, are investigated. Diverse engineering
applications, such as uncertainty propagation, target tracking,
guidance, navigation, and control, are presented to illustrate the
performance of different grid-based estimation techniques.
Grid-based Nonlinear Estimation and its Applications presents new
Bayesian nonlinear estimation techniques developed in the last two
decades. Grid-based estimation techniques are based on efficient
and precise numerical integration rules to improve performance of
the traditional Kalman filtering based estimation for nonlinear and
uncertainty dynamic systems. The unscented Kalman filter,
Gauss-Hermite quadrature filter, cubature Kalman filter,
sparse-grid quadrature filter, and many other numerical grid-based
filtering techniques have been introduced and compared in this
book. Theoretical analysis and numerical simulations are provided
to show the relationships and distinct features of different
estimation techniques. To assist the exposition of the filtering
concept, preliminary mathematical review is provided. In addition,
rather than merely considering the single sensor estimation,
multiple sensor estimation, including the centralized and
decentralized estimation, is included. Different decentralized
estimation strategies, including consensus, diffusion, and
covariance intersection, are investigated. Diverse engineering
applications, such as uncertainty propagation, target tracking,
guidance, navigation, and control, are presented to illustrate the
performance of different grid-based estimation techniques.
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