Grid-based nonlinear estimation and its applications - Jia, Bin, 1982-; Xin, Ming, 1972- (Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019)
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.
5.9 Interacting Multiple Model Filter5.10 Summary; References; 6: Multiple Sensor Estimation; 6.1 Main Fusion Structures; 6.2 Grid-based Information Kalman Filters and Centralized Gaussian Nonlinear Estimation; 6.3 Consensus-based Strategy; 6.3.1 Consensus Algorithm; 6.3.2 Consensus-based Filter; 6.4 Covariance Intersection Strategy; 6.4.1 Covariance Intersection; 6.4.2 Iterative Covariance Intersection; 6.4.3 Distributed Batch Covariance Intersection; 6.4.4 Analysis; 6.5 Diffusion-based Strategy; 6.6 Distributed Particle Filter; 6.7 Multiple Sensor Estimation and Sensor Allocation
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Keywords: Estimation theory; Nonlinear theories; MATHEMATICS / Applied; SCIENCE / Life Sciences / General; TECHNOLOGY / Electricity