John Peake Trimble Navigation, United States
Wouter Pelgrum Blue Origin LLC, United States
Boris Pervan Illinois Institute of Technology, United States
Mark Psiaki Virginia Tech, United States
Sam Pullen Stanford University, United States
Arun Raghupathy NextNav LLC, United States
Vyasaraj Rao Accord Software and Systems, India
John F. Raquet Integrated Solutions for Systems, United States
Tyler G. R. Reeid Stanford University, United States
Charles Rino University of Colorado Boulder, United States
Chris Rizos University of New South Wales, Australia
José Ángel Ávila Rodríguez European Space Agency, the Netherlands
Giulio Ruffini Starlab, Spain
Takeyasu Sakai National Institute of Maritime, Port, and Aviation Technology, Japan
Charles Schue, III UrsaNav, Inc., United States
Logan Scott LS Consulting, United States
James Sennott Tracking and Imaging Systems, United States
Tesalee K. Sensibaugh University of Wyoming, United States
Suneel Sheikh ASTER Labs, Inc., United States
Stephen P. Smith The Charles Stark Draper Laboratory Inc., United States
Andrey Soloviev QuNav, United States
James J. Spilker Jr. Stanford University, United States
Thomas A. Stansell, Jr. Stansell Consulting, United States
Peter Steigenberger German Aerospace Center, Germany
Nikolai Testoedov PNT Center, Russia
Peter J. G. Teunissen Curtin University, Australia and Delft University of Technology, The Netherlands
Sarang Thombre Finnish Geospatial Research Institute, Finland
Charles Toth The Ohio State University, United States
Andrei Tyulin PNT Center, Russia
Sabrina Ugazio Ohio University, United States
Frank van Diggelen Google, United States
Frank van Graas Ohio University, United States
Panagiotis Vergados Jet Propulsion Laboratory, United States
Michael J. Veth Veth Research Associates, United States
Todd Walter Stanford University, United States
Shimon Wdowinski Florida International University, United States
David Whelan University of California San Diego, United States
Walton Williamson Jet Propulsion Laboratory, United States
Chun Yang Sigtem Technology Inc., United States
Rong Yang Shanghai Jiaotong University, China
Zhe Yang University of Colorado Boulder, United States
Zheng Yao Tsinghua University, China
Steven Young National Aeronautics and Space Administration, United States
Valery U. Zavorotny University of Colorado Boulder, and National Oceanic and Atmospheric Administration, United States
Zhen Zhu East Carolina University, United States
35 Overview of Volume 2: Integrated PNT Technologies and Applications
John F. Raquet
Integrated Solutions for Systems, United States
There is little doubt that global navigation satellite systems (GNSS) have changed the way that we think about and use navigation systems. Prior to GPS and other GNSSs, the use of systems which could automatically (without human intervention) determine their own position was generally limited to large, expensive platforms such as aircraft or ships, and even these types of vehicles often required human navigators to assist in the task of navigation. This has all changed with the advent of GNSS, however.
Thanks to GNSS, most people have now become accustomed to their smartphone or vehicle knowing exactly where it is as a part of their everyday lives, and this capability has been built into our expectations. Just as we expect the lights to come on when we turn on a light switch, we also expect a GNSS position fix whenever we turn on a smartphone or other navigation device. This reliance on GNSS goes well beyond obvious navigation devices – we very much depend on many systems which heavily use GNSS for timing purposes, such as banking, communications, and our power grid.
Some have said that navigation is addictive – no matter how much accuracy or availability you have, you always want more. The extreme success of GNSS has, ironically, led to a desire to complement GNSS with other types of sensors for situations in which GNSS is not available, in order to guarantee (as much as is possible) the ability to determine time or position.
Volume 2 focuses in on many of these complementary navigation systems and methods and how they are integrated together to obtain the desired performance. Before diving into the details, it can be helpful to step back and look at the big picture of what is really happening within navigation systems, in order to better understand how the various approaches relate to each other. To do this, it is helpful to develop a “navigation framework.”
35.1 Generalized Navigation Framework
Fundamentally, virtually every navigation system operates the same way. This can be expressed as a predict–observe–compare cycle, as shown in Figure 35.1. The “Navigation State” at the lower right represents the user’s current navigation state, or all of the information about the user’s position, velocity, and so on, as well as estimates of that information’s quality. This can be thought of as the system’s best guess of the user’s position as well as how accurate the system thinks the guess is. As depicted in the “Sensor” box on the left, the system takes a measurement or makes an observation which gives some insight into the user’s navigation state. For GPS, perhaps the system observes the range to a satellite. The system also uses a model of the real world, depicted with the “World Model” box in the upper
