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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Position Navigation and Timing Technologies in the 21st Century - изображение 318

The normalized signal component of the discriminator function Position Navigation and Timing Technologies in the 21st Century - изображение 319is shown in Figure 38.18for t eml= {0.25, 0.5, 1, 1.5, 2}.

It can be seen from Figure 38.18that for small values of the discriminator function can be approximated by a linear function given by - фото 320, the discriminator function can be approximated by a linear function given by

where α is the slope of the discriminator function at Δ t k 0 57 which is - фото 321

where α is the slope of the discriminator function at Δ t k= 0 [57], which is obtained by

Since R c τ is symmetric and the linearized discriminator output becomes - фото 322

Since R c( τ ) is symmetric,

and the linearized discriminator output becomes 3812 It is worth noting - фото 323

and the linearized discriminator output becomes

(38.12) It is worth noting that R c τ and R c τ are obtained by numerically - фото 324

It is worth noting that R c( τ ) and R c′ ( τ ) are obtained by numerically computing the autocorrelation function of the pulse‐shaped short code. Since the FIR of the pulse shaping filter h [ k ] is defined over only 48 values of k , the autocorrelation function R c( τ ) will be defined over 95 values of τ . However, interpolation may be used to evaluate R c( τ ) and R c′ ( τ ) at any τ . The mean and variance of D kcan be obtained from Eq. (38.12), and are given by

(38.13) 3814 - фото 325

3814 Now that the discriminator statistics are known - фото 326 3814 Now that the discriminator statistics are known the closedloop - фото 327

(38.14) Now that the discriminator statistics are known the closedloop pseudorange - фото 328

Now that the discriminator statistics are known, the closed‐loop pseudorange error is characterized next.

38.5.3.2 Closed‐Loop Analysis

In order to achieve the desired loop noise‐equivalent bandwidth, K in Eq. (38.11)must be normalized according to

Figure 3818 Output of the coherent baseband discriminator function for the - фото 329

Figure 38.18 Output of the coherent baseband discriminator function for the CDMA short code with different correlator spacings (Khalife et al. [12]).

Source: Reproduced with permission of IEEE.

(38.15) In cellular CDMA systems for a t emlof 12 the loop filter gain becomes K 4 - фото 330

In cellular CDMA systems, for a t emlof 1.2, the loop filter gain becomes K ≈ 4 B n, DLL; hence, the choice of K in Section 38.5.2.3. Assuming a zero‐mean tracking error, that is, Position Navigation and Timing Technologies in the 21st Century - изображение 331, the variance of the code start time error is given by

At steady state varΔ t k 1 becomes 3816 where Δ t is the steadystate - фото 332

At steady state, var{Δ t k + 1} becomes

(38.16) where Δ t is the steadystate code start time error Combining Eqs 3815 - фото 333

where Δ t is the steady‐state code start time error. Combining Eqs. (38.15)– (38.16)yields

(38.17) The pseudorange can hence be expressed as where v - фото 334

The pseudorange can hence be expressed as where v k is a zeromean random - фото 335

The pseudorange can hence be expressed as

where v k is a zeromean random variable with variance σ 2 c 2 var Δ t - фото 336

where v ( k ) is a zero‐mean random variable with variance σ 2= c 2· var {Δ t }. Figure 38.19shows a plot of the standard deviation of Δ t , denoted by σ , as a function of the carrier‐to‐noise ratio C / N 0for t eml= 1.25 chips.

38.5.4 Cellular CDMA Navigation Experimental Results

This section presents experimental results for navigation with cellular CDMA signals. These results did not suffer from the BTS sector clock discrepancy issue (discussed in Section 38.7), since signals from only one sector antenna in each BTS cell were used. Experimental results exhibiting the BTS sector clock discrepancy issue and mitigation approaches are studied in [23, 25]. Section 38.5.4.1analyzes the pseudorange obtained by the receiver discussed in Section 38.5.2. Sections 38.5.4.2and 38.5.4.3present navigation results with aerial and ground vehicles, respectively.

Figure 3819 Plot of σ the standard deviation of Δ t as a function of the - фото 337

Figure 38.19 Plot of σ , the standard deviation of Δ t , as a function of the carrier‐to‐noise ratio картинка 338for t eml= 1.25 chips and B n,DLL= {0.5 Hz, 0.05 Hz} (Khalife et al. [12]).

Source: Reproduced with permission of IEEE.

38.5.4.1 Pseudorange Analysis

The variation in the pseudorange obtained by the receiver discussed in Section 38.5.2is compared to the variation in the true range between a mobile receiver and cellular CDMA BTSs. For this purpose, the receiver was mounted on two platforms: (i) an unmanned aerial vehicle (UAV) and (ii) a ground vehicle [12, 18, 25].

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