Position, Navigation, and Timing Technologies in the 21st Century. Группа авторов

timing Example of signal of opportunity (SoOP), reference receiver sometimes required Terrestrial beacon receiver Navigation signals from terrestrial beacons Beacon locations, signal structure, signal timing Requires dedicated infrastructure, more design flexibility than SoOP Digital TV receiver Digital TV signals Transmitter locations, signal timing Example of SoOP, reference receiver sometimes required Low‐frequency receiver Low‐frequency RF signals Transmitter location or direction of arrival, local distortion effects Susceptible to local distortions, generally less accurate than higher frequency/wider bandwidth signals Radar RF signals Locations of identifiable RF reflectors for absolute positioning Generally larger/higher power than receiver‐based systems Low‐Earth orbit (LEO) satellite receiver Signals from LEO satellites LEO satellite position/velocity, signal timing (in some cases), atmospheric models Greater geometric/signal diversity and higher received power than GNSS Inertial Rotation and specific force Gravitational field Dead‐reckoning only – drift normally requires update GNSS RF signals from satellites Satellite ephemeris and clock errors, atmospheric models Ideal for updating inertial Magnetometer Magnetic field (including variations) Magnetic field map Local (vehicle) effects calibration may be required LiDAR Range and intensity of laser returns Shape/location of objects being sensed Can be used in dead‐reckoning or absolute modes Cameras Intensity of light as a function of direction Map of image features or three‐dimensional image model for absolute positioning Can be used in dead‐reckoning or absolute modes X‐ray detector X‐ray signals coming from pulsars Knowledge of pulsar directions and signal characteristics (including timing) Positioning is based on signal time of arrival Clock Varies by clock type Perhaps calibration parameters Measures rate of time passage (frequency), and if initialized and integrated, absolute time

35.2 Summary of Content of Volume 2

      Volume 2 begins with an overview of nonlinear estimation techniques (Chapter 36), which are often required when integrating complementary navigation sensors. This chapter also lays the groundwork for the estimation strategies that are described in subsequent chapters.

      The next group of chapters covers a variety of RF‐based complementary navigation techniques. Many of the principles and algorithmic approaches for indoor navigation are summarized in Chapter 37, as well as a survey of different types of indoor navigation sensors and phenomenologies. This is followed by several chapters which describe in detail a variety of RF signals, including cellular (Chapter 38), terrestrial navigation beacons (Chapter 39), digital television (Chapter 40), low‐frequency systems (Chapter 41), radar (Chapter 42), and RF signals from low‐Earth orbiting (LEO) satellites (Chapter 43).

      There are two chapters that describe inertial technology: a general introduction to INS (Chapter 44) and MEMS inertial systems (Chapter 45). The introduction chapter provides an overview of inertial systems. It describes the fundamental mechanisms of various accelerometers and gyroscopes that are the building blocks of INS, their error characteristics and performances, and outlook of technology advancement. The focus of MEMS inertial sensors is to reduce the cost, size, weight, and power when compared to existing inertial sensors. Doing so would expand the applications in which it is feasible to leverage inertial technology.

It is important to recognize that inertial systems cannot operate without aiding from additional sensors, other than for short time periods. The primary reason for this is that inertial systems are unstable in the vertical channel, so at a minimum they need some sort of aiding of the vertical channel (such as a barometric altimeter or terrain height aiding). Even if the vertical channel is aided, the horizontal directions will drift in an inertial system, with the rate of drift determined by the quality of the system and the accuracy of the initialization of the attitude and position of the system. (Even if an INS had perfect gyroscopes and accelerometers, there would still be growing error due to imperfections in our knowledge of gravity).

      Probably the most common sensor used to aid an inertial is a GNSS receiver. Chapter 46 describes classic approaches for integrating GPS with INS, including loose and tight integration. It also describes a different way of thinking about the GPS/INS integration problem, in which there is more emphasis on using carrier‐phase measurements to provide velocity‐like updates to the INS, with additional correction from the pseudorange measurements.

      Clock has been an essential sensor for navigation since ancient times. The accuracy and stability of clocks continue the improve in recent decades. Chapter 47 provides an overview of recent technology development in atomic clocks for GNSS.

      An approach for using knowledge of the variation in Earth’s magnetic field for absolute positioning using a magnetometer is described in Chapter 48. This method works indoors, on a ground vehicle, and in an aircraft, and this chapter describes the differences between these different environments and shows examples of working systems in each case.

      Next, the use of LiDAR for navigation is described in Chapter 49. Various types of LiDARs are considered, as well as different ways in which LiDAR data can be leveraged for navigation purposes. This chapter also describes features that can be identified using LiDAR data, and how those features can be incorporated into an integrated navigation system. Both dead‐reckoning and absolute positioning/attitude approaches are considered.