Position, Navigation, and Timing Technologies in the 21st Century

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Covers the latest developments in PNT technologies, including integrated satellite navigation, sensor systems, and civil applications Featuring sixty-four chapters that are divided into six parts, this two-volume work provides comprehensive coverage of the state-of-the-art in satellite-based position, navigation, and timing (PNT) technologies and civilian applications. It also examines alternative navigation technologies based on other signals-of-opportunity and sensors and offers a comprehensive treatment on integrated PNT systems for consumer and commercial applications.
Volume 1 of
contains three parts and focuses on the satellite navigation systems, technologies, and engineering and scientific applications. It starts with a historical perspective of GPS development and other related PNT development. Current global and regional navigation satellite systems (GNSS and RNSS), their inter-operability, signal quality monitoring, satellite orbit and time synchronization, and ground- and satellite-based augmentation systems are examined. Recent progresses in satellite navigation receiver technologies and challenges for operations in multipath-rich urban environment, in handling spoofing and interference, and in ensuring PNT integrity are addressed. A section on satellite navigation for engineering and scientific applications finishes off the volume.
Volume 2 of
consists of three parts and addresses PNT using alternative signals and sensors and integrated PNT technologies for consumer and commercial applications. It looks at PNT using various radio signals-of-opportunity, atomic clock, optical, laser, magnetic field, celestial, MEMS and inertial sensors, as well as the concept of navigation from Low-Earth Orbiting (LEO) satellites. GNSS-INS integration, neuroscience of navigation, and animal navigation are also covered. The volume finishes off with a collection of work on contemporary PNT applications such as survey and mobile mapping, precision agriculture, wearable systems, automated driving, train control, commercial unmanned aircraft systems, aviation, and navigation in the unique Arctic environment.
In addition, this text:
Serves as a complete reference and handbook for professionals and students interested in the broad range of PNT subjects Includes chapters that focus on the latest developments in GNSS and other navigation sensors, techniques, and applications Illustrates interconnecting relationships between various types of technologies in order to assure more protected, tough, and accurate PNT
will appeal to all industry professionals, researchers, and academics involved with the science, engineering, and applications of position, navigation, and timing technologies.pnt21book.com

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Source: Reproduced with permission of IEEE, ION.

Position Navigation and Timing Technologies in the 21st Century - изображение 201

where Position Navigation and Timing Technologies in the 21st Century - изображение 202and The clock bias is estimated by solving a weighted leastsquares WLS - фото 203. The clock bias is estimated by solving a weighted leastsquares WLS problem resulting in - фото 204is estimated by solving a weighted least‐squares (WLS) problem, resulting in the estimate

and the associated estimation error variance where Wis the weighting matrix - фото 205

and the associated estimation error variance Position Navigation and Timing Technologies in the 21st Century - изображение 206, where Wis the weighting matrix. The true clock bias of the i‐ th BTS can now be expressed as Position Navigation and Timing Technologies in the 21st Century - изображение 207, where w iis a zero‐mean Gaussian random variable with variance картинка 208.

Since the navigating receiver is using the estimate of the BTS clock bias, which is produced by the mapping receiver, the pseudorange measurement made by the navigating receiver on the i ‐th BTS becomes

Position Navigation and Timing Technologies in the 21st Century - изображение 209

where Position Navigation and Timing Technologies in the 21st Century - изображение 210and η i≜ v i− w imodels the overall uncertainty in the pseudorange measurement. Hence, the vector Position Navigation and Timing Technologies in the 21st Century - изображение 211is a zero‐mean Gaussian random vector with a covariance matrix = C+ R, where Position Navigation and Timing Technologies in the 21st Century - изображение 212is the covariance matrix of Position Navigation and Timing Technologies in the 21st Century - изображение 213and Position Navigation and Timing Technologies in the 21st Century - изображение 214is the covariance of the measurement noise vector Position Navigation and Timing Technologies in the 21st Century - изображение 215. The Jacobian matrix Hof the nonlinear measurements Position Navigation and Timing Technologies in the 21st Century - изображение 216with respect to Position Navigation and Timing Technologies in the 21st Century - изображение 217is given by Position Navigation and Timing Technologies in the 21st Century - изображение 218, where

The navigating receivers state can now be estimated by solving a WNLS problem - фото 219

The navigating receiver’s state can now be estimated by solving a WNLS problem. The WNLS equations are given by

where l is the iteration number and denotes the nonlinear m - фото 220 where l is the iteration number and denotes the nonlinear measurements - фото 221

where l is the iteration number, and картинка 222denotes the nonlinear measurements картинка 223evaluated at the current estimate картинка 224.

38.4.2 Radio SLAM Framework

A dynamic estimator, such as an extended Kalman filter (EKF), can be used in the radio SLAM framework for stand‐alone receiver navigation (i.e. without a mapper). Certain a priori knowledge about the BTSs’ and/or receiver’s states must be satisfied to make the radio SLAM estimation problem observable [27, 40–42].

To demonstrate a particular formulation of the radio SLAM framework, consider the simple case where the BTSs’ positions are known. Also, assume the receiver’s initial state vector to be known (e.g. from a GNSS navigation solution). Using the pseudoranges ( Eq. (38.3)), the EKF will estimate the state vector composed of the receiver’s position картинка 225and velocity as well as the difference between the receivers clock bias and each BTS and - фото 226as well as the difference between the receiver’s clock bias and each BTS and the difference between the receiver’s clock drift and each BTS, specifically

where δt rand are the receivers and the i t - фото 227

where δt rand are the receivers and the i th BTS clock bias respectively and - фото 228; δt rand картинка 229are the receiver’s and the i ‐th BTS clock bias, respectively; and картинка 230and картинка 231are the receiver’s and the i ‐th BTS clock drift, respectively.

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