Grid-based Nonlinear Estimation and Its Applications

<p><em>Grid-based Nonlinear Estimation and its Applications</em> 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. </p><p>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. </p>