Fundamentals of Kalman Filtering: A Practical Approach (Progress in Astronautics & Aeronautics, Band 208)

The algorithmic technique of Kalman filtering has been applied in a wide range of applications, including spacecraft navigation, the prediction of short-term stock market fluctuations, and hand-held global positioning systems. Zarchan (MIT Lincoln Laboratory) and the late Musoff (recently of the Charles Stark Draper Laboratory) demonstrate how to build Kalman filters by example, spending significant time in each case on the preliminary, but important, issue of setting up the problem. This new edition adds chapters on the fading-memory filter and on simplified techniques for improving performance. Coverage includes the numerical basics, least squares, recursive least-squares filtering, polynomial and nonpolynomial Kalman filters, extended Kalman filtering, drag and falling object problems, cannon-launched projectile tracking, among other topics. Also added to this edition is an appendix summarizing important concepts and formulas. Annotation ©2004 Book News, Inc., Portland, OR (booknews.com)

Paul Zarchan has a BSEE degree from the City College of New York and an MSEE degree from Columbia University. He has more than 30 years' experience in the missile guidance and control field and has worked as Principal Engineer for Raytheon, served as Senior Research Engineer with the Israeli Ministry of Defense and is currently a Principal Member of the Technical Staff at the Charles Stark Draper Laboratory.

Dr. Howard Musoff has a BSEE degree from the City College of New York, an MSEE degree from Northeastern University, and an Sc.D.degree from MIT. He is a Principal Member of the Technical Staff at the Charles Stark Draper Laboratory, were he has been employed for more than 40 years. Among other tasks, he designs Kalman filters for applications in the filed of inertial navigation. Dr. Musoff is also a co-holder of two patents in that field.