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The ®ve basic forms of navigation are as follows: 1. Pilotage, which essentially relies on recognizing landmarks to know where you are. It is older than human kind. 2. Dead reckoning, which relies on knowing where you started from, plus some form of heading information and some estimate of speed. 3. Celestial navigation, using time and the angles between local vertical and known celestial objects (e.g., sun, moon, or stars) [115]. 4. Radio navigation, which relies on radio-frequency sources with known locations (including Global Positioning System satellites). | Global Positioning Systems Inertial Navigation and Integration Mohinder S. Grewal Lawrence R. Weill Angus P. Andrews Copyright 2001 John Wiley Sons Inc. Print ISBN 0-471-35032-X Electronic ISBN 0-471-20071-9 1 Introduction The five basic forms of navigation are as follows 1. Pilotage which essentially relies on recognizing landmarks to know where you are. It is older than human kind. 2. Dead reckoning which relies on knowing where you started from plus some form of heading information and some estimate of speed. 3. Celestial navigation using time and the angles between local vertical and known celestial objects e.g. sun moon or stars 115 . 4. Radio navigation which relies on radio-frequency sources with known locations including Global Positioning System satellites . 5. Inertial navigation which relies on knowing your initial position velocity and attitude and thereafter measuring your attitude rates and accelerations. It is the only form of navigation that does not rely on external references. These forms of navigation can be used in combination as well 16 135 . The subject of this book is a combination of the fourth and fifth forms of navigation using Kalman filtering. Kalman filtering exploits a powerful synergism between the Global Positioning System GPS and an inertial navigation system INS . This synergism is possible in part because the INS and GPS have very complementary error characteristics. Short-term position errors from the INS are relatively small but they degrade without bound over time. GPS position errors on the other hand are not as good over the short term but they do not degrade with time. The Kalman filter is able to take advantage of these characteristics to provide a common integrated navigation 1 2 INTRODUCTION implementation with performance superior to that of either subsystem GPS or INS . By using statistical information about the errors in both systems it is able to combine a system with tens of meters position uncertainty GPS with .
