Grid-based Nonlinear Estimation and Its Applications

By: Bin Jia, Ming Xin

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Paperback | 31 March 2021 | Edition Number 1

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260 Pages

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23.4 x 15.6 x 1.4

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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.

Industry Reviews

"This book is a comprehensive account on one such practical estimation technique, based on approximation of the conditional distribution by mixtures of Gaussian densities and replacing the emerging integrals by grid-based numerical schemes. In summary, this book is a carefully written guide to a particular approach to the approximation of optimal estimation algorithms and its implementation in concrete real-life applications."
- Pavel Chigansky, Mathematical Reviews Clippings, July 2020

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