Overview

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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9780367779955

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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

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

Author Information

Bin Jia is a Project Manager at Intelligent Fusion Technology, Inc. in Germantown, Maryland, a research and development company focusing on information fusion technologies from fundamental research to industry transition and product development and support. Dr. Jia received a Ph.D. in Aerospace Engineering from Mississippi State University in 2012, a M.S from Graduate University of the Chinese Academy of Sciences, and a B.S from Jilin University, China, in 2007 and 2004, respectively. From 2012 to 2013, he worked as a postdoctoral research scientist at Columbia University. Dr. Jia’s research experience includes Bayesian estimation, multi-sensor multi-target tracking, information fusion, guidance and navigation, and space situational awareness. Ming Xin is an Associate Professor in the Department of Mechanical and Aerospace Engineering at University of Missouri-Columbia. He received his B.S. and M.S. degrees from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 1993 and 1996, respectively, both in Automatic Control. He received his Ph.D. in Aerospace Engineering from Missouri University of Science and Technology in 2002. His research interests include guidance, navigation, and control of aerospace vehicles, flight mechanics, estimation theory and applications, cooperative control of multi-agent systems, and sensor networks. Dr. Xin was the recipient of the National Science Foundation CAREER Award in 2009. He is an Associate Fellow of AIAA and a Senior Member of IEEE and AAS.

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