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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The long code is generated by masking the outputs of the 42 registers and computing the modulo‐two sum of the resulting bits. In contrast to the short code generator in cellular CDMA and the C/A code generator in GPS, the 42 long code generator registers are configured to satisfy a linear recursion given by

The long code mask is obtained by combining the PN offset and the paging - фото 297 The long code mask is obtained by combining the PN offset and the paging - фото 298

The long code mask is obtained by combining the PN offset and the paging channel number p as shown in Figure 38.16.

Figure 3814 Cellular CDMA signal tracking a code phase error chips b - фото 299

Figure 38.14 Cellular CDMA signal tracking: (a) code phase error (chips), (b) carrier phase error (degrees), (c) Doppler frequency estimate (hertz), (d) prompt (black), early (red), and late (green) correlation, (e) measured pseudorange (m), and (f) correlation function (Khalife et al. [18]).

Source: Reproduced with permission of IEEE.

Figure 3815 Sync and paging channel timing Khalife et al 18 3GPP2 50 - фото 300

Figure 38.15 Sync and paging channel timing (Khalife et al. [18]; 3GPP2 [50]).

Source: Reproduced with permission of IEEE.

Subsequently, the sync message is decoded first, and the PN offset, the paging channel number, and the long code state are then used to descramble and decode the paging message. It is important to note that the long code is first decimated at a rate of 1/64 to match the paging channel symbol rate. More details are specified in [47]. Figure 38.17shows the demodulated sync signal as well as the final information decoded from the sync and paging channels. Note that the shown signal corresponds to the US cellular provider Verizon, which does not broadcast its BTS position information (latitude and longitude). Moreover, note that the last digit in the BTS ID corresponds to the sector number of the BTS cell. This is important for data association purposes, since different sectors of the same BTS cell are not perfectly synchronized. This is discussed in more detail in Section 38.7.

Figure 3816 Long code mask structure Khalife et al 18 3GPP2 50 - фото 301

Figure 38.16 Long code mask structure (Khalife et al. [18]; 3GPP2 [50]).

Source: Reproduced with permission of IEEE.

Figure 3817 Message decoding demodulated sync channel signal left and BTS - фото 302

Figure 38.17 Message decoding: demodulated sync channel signal (left) and BTS and system information decoded from sync and paging channels (right) (Khalife et al. [18]).

Source: Reproduced with permission of IEEE.

38.5.3 Code Phase Error Analysis

Section 38.5.2presented a recipe for designing a receiver that can extract a pseudorange estimate from cellular CDMA signals. This section analyzes the statistics of the error of the code phase estimate for a coherent DLL. It is worth noting that when the receiver is closely tracking the carrier phase, the non‐coherent dot‐product discriminator and a coherent DLL discriminator will perform similarly. Hence, for simplicity, the analysis is carried out for a coherent baseband discriminator. To this end, it is assumed that t sis constant. Therefore, the carrier aiding term will be negligible, and the code start time error Δ t kwill be affected only by the channel noise. As mentioned in Section 38.5.2.3, it is enough to use a first‐order loop for the DLL, yielding the following closed‐loop time‐update error equation [57]:

(38.11) where e DLL kis the output of the code phase discriminator The discriminator - фото 303

where e DLL, kis the output of the code phase discriminator. The discriminator statistics are discussed next.

38.5.3.1 Discriminator Statistics

In order to study the discriminator statistics, the received signal noise statistics must first be determined. In what follows, the received signal noise is characterized for an additive white Gaussian noise channel.

Received Signal Noise Statistics:To make the analysis tractable, the continuous‐time received signal and correlation are considered. The transmitted signal is assumed to propagate in an additive white Gaussian noise channel with a power spectral density The continuoustime received signal after downmixing and bandpass sampling - фото 304. The continuous‐time received signal after down‐mixing and bandpass sampling is given by

and the continuoustime matchedfiltered baseband signal x t is given by - фото 305

and the continuous‐time matched‐filtered baseband signal x ( t ) is given by

The resulting early and late correlations in the DLL are given by - фото 306

The resulting early and late correlations in the DLL are given by

Position Navigation and Timing Technologies in the 21st Century - изображение 307 Position Navigation and Timing Technologies in the 21st Century - изображение 308

where Position Navigation and Timing Technologies in the 21st Century - изображение 309and Position Navigation and Timing Technologies in the 21st Century - изображение 310. Assuming the receiver is closely tracking the carrier phase [55], the early and late correlations may be approximated with

where and - фото 311 where and are zeromean Gaussian random variables with the following var - фото 312

where картинка 313and are zeromean Gaussian random variables with the following variances and - фото 314are zero‐mean Gaussian random variables with the following variances and covariances:

Coherent Discriminator StatisticsThe coherent - фото 315 Coherent Discriminator StatisticsThe coherent baseband discriminator function - фото 316 Coherent Discriminator StatisticsThe coherent baseband discriminator function - фото 317

Coherent Discriminator Statistics:The coherent baseband discriminator function is defined as

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