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Global Positioning Systems, Inertial Navigation, and Integration

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An updated guide to GNSS and INS, and solutions to real-world GPS/INS problems with Kalman filtering

Written by recognized authorities in the field, this second edition of a landmark work provides engineers, computer scientists, and others with a working familiarity with the theory and contemporary applications of Global Navigation Satellite Systems (GNSS), Inertial Navigational Systems (INS), and Kalman filters. Throughout, the focus is on solving real-world problems, with an emphasis on the effective use of state-of-the-art integration techniques for those systems, especially the application of Kalman filtering. To that end, the authors explore the various subtleties, common failures, and inherent limitations of the theory as it applies to real-world situations, and provide numerous detailed application examples and practice problems, including GNSS-aided INS, modeling of gyros and accelerometers, and SBAS and GBAS.

Drawing upon their many years of experience with GNSS, INS, and the Kalman filter, the authors present numerous design and implementation techniques not found in other professional references. This Second Edition has been updated to include:

  • GNSS signal integrity with SBAS
  • Mitigation of multipath, including results
  • Ionospheric delay estimation with Kalman filters
  • New MATLAB programs for satellite position determination using almanac and ephemeris data and ionospheric delay calculations from single and dual frequency data
  • New algorithms for GEO with L1 /L5 frequencies and clock steering
  • Implementation of mechanization equations in numerically stable algorithms

To enhance comprehension of the subjects covered, the authors have included software in MATLAB, demonstrating the working of the GNSS, INS, and filter algorithms. In addition to showing the Kalman filter in action, the software also demonstrates various practical aspects of finite word length arithmetic and the need for alternative algorithms to preserve result accuracy.

About the Author

MOHINDER S. GREWAL, PhD, PE, is Professor of Electrical Engineering in theCollege of Engineering and Computer Science at California State University, Fullerton.

LAWRENCE R. WEILL, PhD, is Professor Emeritus of Applied Mathematics in the College of Mathematics and Natural Sciences at California State University, Fullerton.

ANGUS P. ANDREWS, PhD, is Senior Scientist (Retired) at the Rockwell Science Center in Thousand Oaks, California.

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