## Optimal State Estimation: Kalman, H Infinity, and Nonlinear ApproachesA bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries. |

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Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches Dan Simon No preview available - 2006 |

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algorithm approximation Chapter computational effort constant constraints continuous-time continuous-time system cost function cross covariance deﬁned discrete-time discrete-time system discussed dynamics eigenvalues eigenvectors equal estimation error estimation theory estimation-error covariance example expected value filter ﬁlter performance ﬁnd ﬁrst follows Gaussian gives H00 ﬁlter IEEE Transactions implementation initial input integration Kalman ﬁlter equations Kalman gain least squares linear systems matrix exponential matrix inversion lemma mean and covariance measurement equation measurement noise measurement update minimizes nonlinear system obtain Optimal State Estimation parameter particle ﬁlter positive deﬁnite positive semideﬁnite posteriori priori probability process noise random variable reduced-order Riccati equation robust satisﬁes scalar scalar system Section sigma points simulation smoothing solution solve standard deviation standard Kalman ﬁlter steady-state Kalman ﬁlter steady-state value stochastic process Suppose symmetric system model Taylor series Theorem update equation variance vector white noise written zero zero-mean

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Page 5 - B are said to be conformable in the order AB if and only if the number of columns in A is equal to the number of rows in B.

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Page 16 - There are three classes of corn, of which three bundles of the first class, two of the second and one of the third make 39 measures. Two of the first, three of the second and one of the third make 34 measures. And one of the first, two of the second and three of the third make 26 measures. How many measures of grain are contained in one bundle of each class?