kalman filtering下载

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ukf，卡尔曼滤波程序，目标跟踪，matlab
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Two modern topics in Kalman filtering are new additions to this Third Edition of Kalman Filtering with Real-Time Appli- cations. Interval Kalman Filtering (Chapter 10) is added to ex- pand the capability' of Kalman filtering to uncertain systems, and Wavelet Kalman Filtering (Chapter 11) is introduced to incorpo- rate efficient techniques from wavelets and splines with Kalman filtering to give more effective computational schemes for treating problems in such areas as signal estimation and signal decompo- sition. It is hoped that with the addition of these two new chap- ters, the current edition gives a more complete and up-to-date treatment of Kalman filtering for real-time applications. College Station and Houston August 1998 Charles K. Chui Guanrong Chen

The definitive textbook and professional reference on Kalman Filtering - fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.

classical textbook of Kalman Filtering. With the possible exception of the fast Fourier transform, Kalman filtering is probably the most important algorithmic technique ever devised.

Organized for use as a text for an introductory course in stochastic processes at the senior level and as a first-year, graduate-level course in Kalman filtering theory and applications, this book includes real-world problems in practice as illustrative examples, and also covers the more practical aspects of implementation. The author Grewal teaches at Cal State Fullerton and also offers seminars and tutorials on Kalman Filters. Dr. Grewal has contributed the Article on Kalman Filters for the Webster Encyclopedia

This is a practical guide to building Kalman filters that shows how the filtering equations can be applied to real-life problems. Numerous examples are presented in detail, showing the many ways in which Kalman filters can be designed. Computer code written in FORTRAN, MATLAB®, and True BASIC accompanies all of the examples so that the interested reader can verify concepts and explore issues beyond the scope of the text. In certain instances, the authors intentionally introduce mistakes to the initial filter designs to show the reader what happens when the filter is not working properly. The text carefully sets up a problem before the Kalman filter is actually formulated, to give the reader an intuitive feel for the problem being addressed. Because real problems are seldom presented as differential equations, and usually do not have unique solutions, the authors illustrate several different filtering approaches. Readers will gain experience in software and performance tradeoffs for determining the best filtering approach. The material that has been added to this edition is in response to questions and feedback from readers. The third edition has three new chapters on unusual topics related to Kalman filtering and other filtering techniques based on the method of least squares.Chapter 17 presents a type of filter known as the fixed or finite memory filter, which only remembers a finite number of measurements from the past. Chapter 18 shows how the chain rule from calculus can be used for filter initialization or to avoid filtering altogether. A realistic three-dimensional GPS example is used to illustrate the chain-rule method for filter initialization. Finally, Chapter 19 shows how a bank of linear sine-wave Kalman filters, each one tuned to a different sine-wave frequency, can be used to estimate the actual frequency of noisy sinusoidal measurements and obtain estimates of the states of the sine wave when the measurement noise is low.

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