Chapter 1: INTRODUCTION

INTRODUCTION

There are five basic forms of navigation:

Pilotage, which essentially relies on recognizing landmarks to know where
you are and how you are oriented. It is older than humankind.
Dead reckoning, which relies on knowing where you started from, plus
some form of heading information and some estimate of speed.
Celestial navigation, using time and the angles between local vertical and
known celestial objects (e.g., sun, moon, planets, stars) to estimate orientation,
latitude, and longitude [186].
Radio navigation, which relies on radiofrequency sources with known locations
(including global navigation satellite systems satellites).
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 [18, 26, 214]. The subject of this book is a combination of the fourth and fifth forms of navigation using Kalman filtering.

1.1 GNSS/INS INTEGRATION OVERVIEW

Kalman filtering exploits a powerful synergism between the global navigation satellite systems (GNSSs) and an inertial navigation system (INS). This synergism is possible, in part, because the INS and GNSS have very complementary error characteristics. Short-term position errors from the INS are relatively small, but they degrade without bound over time. GNSS 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 implementation with performance superior to that of either subsystem (GNSS 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 (GNSS) with another system whose position uncertainty degrades at kilometers per hour (INS) and achieve bounded position uncertainties in the order of centimeters [with differential GNSS (DGNSS)] to meters.

A key function performed by the Kalman filter is the statistical combination of GNSS and INS information to track drifting parameters of the sensors in the INS. As a result, the INS can provide enhanced inertial navigation accuracy during periods when GNSS signals may be lost, and the improved position and velocity estimates from the INS can then be used to cause GNSS signal reacquisition to occur much sooner when the GNSS signal becomes available again.

This level of integration necessarily penetrates deeply into each of these subsystems, in that it makes use of partial results that are not ordinarily accessible to users. To take full advantage of the offered integration potential, we must delve into technical details of the designs of both types of systems.

Next Excerpt >

Purchase This Book

TABLE OF CONTENTS

Chapter 1: INTRODUCTION

Contact Preferences

This is embarrasing...

Customize Your GlobalSpec Experience

Select Your Free Newsletters

Industry Newsletters

Select Your Free Product Alerts

This is embarrasing...