Fundamentals of kalman filtering a practical approach second edition

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.

Among other tasks, he designs Kalman filters for applications in the filed of inertial navigation. Musoff is also a co-holder of two patents in that field. Your email address will not be published. This site uses Akismet to reduce spam.

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Responsibility Paul Zarchan, Howard Musoff. Edition 3rd ed. Imprint Reston, Va. Physical description xxvii, p.

Series Progress in astronautics and aeronautics v. Available online. Full view. SAL3 off-campus storage. P75 V. More options. Find it at other libraries via WorldCat Limited preview. Contributor Musoff, Howard. American Institute of Aeronautics and Astronautics. Bibliography Includes bibliographical references and index.

Contents Numerical basics Method of least squares Recursive least-squares filtering Polynomial Kalman filters Kalman filters in a nonpolynomial world Continuous polynomial Kalman filter Extended Kalman filtering Drag and falling object Cannon-launched projectile tracking problem Tracking a sine wave Satellite navigation Biases Linearized Kalman filtering Miscellaneous topics Fading-memory filter Assorted techniques for improving Kalman-filter performance Fixed-memory filters Chain-rule and least-squares filtering Filter bank approach to tracking a sine wave Appendix A: Fundamentals of Kalman-filtering software Appendix B: Key formula and concept summary.

Summary 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. 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.