Fundamentals Of Kalman Filtering: A Practical Approach (Progress in Astronautics & Aeronautics)

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)

• Kalman Filter for Beginners: with MATLAB Examples
• Applied Optimal Estimation (The MIT Press)
• Nonlinear Systems
• Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition (GNSS Technology and Applications)