Mohinder S. Grewal - Global Navigation Satellite Systems, Inertial Navigation, and Integration

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Covers significant changes in GPS/INS technology, and includes new material on GPS,
GNSSs including GPS, Glonass, Galileo, BeiDou, QZSS, and IRNSS/NAViC,
and MATLAB programs on square root information filtering (SRIF)
This book provides readers with solutions to real-world problems associated with global navigation satellite systems, inertial navigation, and integration. It presents readers with numerous detailed examples and practice problems, including GNSS-aided INS, modeling of gyros and accelerometers, and SBAS and GBAS. This revised fourth edition adds new material on GPS III and RAIM. It also provides updated information on low cost sensors such as MEMS, as well as GLONASS, Galileo, BeiDou, QZSS, and IRNSS/NAViC, and QZSS. Revisions also include added material on the more numerically stable square-root information filter (SRIF) with MATLAB programs and examples from GNSS system state filters such as ensemble time filter with square-root covariance filter (SRCF) of Bierman and Thornton and SigmaRho filter.
Global Navigation Satellite Systems, Inertial Navigation, and Integration, 4th Edition Updates on the significant upgrades in existing GNSS systems, and on other systems currently under advanced development Expanded coverage of basic principles of antenna design, and practical antenna design solutions More information on basic principles of receiver design, and an update of the foundations for code and carrier acquisition and tracking within a GNSS receiver Examples demonstrating independence of Kalman filtering from probability density functions of error sources beyond their means and covariances New coverage of inertial navigation to cover recent technology developments and the mathematical models and methods used in its implementation Wider coverage of GNSS/INS integration, including derivation of a unified GNSS/INS integration model, its MATLAB implementations, and performance evaluation under simulated dynamic conditions
is intended for people who need a working knowledge of Global Navigation Satellite Systems (GNSS), Inertial Navigation Systems (INS), and the Kalman filtering models and methods used in their integration.

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In this case, the receiver and two transmitters are located in the same plane, as shown in Figure 2.3, with known positions of the two transmitters: x 1, y 1and x 2, y 2. Ranges R 1and R 2from the two transmitters to the user position are calculated as

(2.1) Global Navigation Satellite Systems Inertial Navigation and Integration - изображение 13

(2.2) Global Navigation Satellite Systems Inertial Navigation and Integration - изображение 14

where

c = speed of light (0.299 792 458 m/ns)
Δ T 1 = time taken for the radiowave to travel from transmitter 1 to the user (ns)
Δ T 2 = time taken for the radiowave to travel from transmitter 2 to the user (ns)
X , Y = unknown user position to be solved for (m)

The range to each transmitter can be written as

(2.3) Figure 23Two transmitters with known 2D positions 24 - фото 15

Figure 23Two transmitters with known 2D positions 24 Expanding R 1and R - фото 16

Figure 2.3Two transmitters with known 2D positions.

(2.4) Expanding R 1and R 2in Taylor series expansion with small perturbation in X by - фото 17

Expanding R 1and R 2in Taylor series expansion with small perturbation in X by Δ x and Y by Δ y yields

(2.5) 26 where u 1and u 2are higherorder terms The derivatives of Eqs - фото 18

(2.6) where u 1and u 2are higherorder terms The derivatives of Eqs 23and - фото 19

where u 1and u 2are higher‐order terms. The derivatives of Eqs. (2.3)and (2.4)with respect to X , Y are substituted into Eqs. (2.5)and (2.6), respectively.

Thus, for the symmetric case, we obtain

(2.7) 28 To obtain the leastsquares estimate of X Y we need to minimize - фото 20

(2.8) Global Navigation Satellite Systems Inertial Navigation and Integration - изображение 21

To obtain the least‐squares estimate of ( X , Y ), we need to minimize the quantity

(2.9) Global Navigation Satellite Systems Inertial Navigation and Integration - изображение 22

which is

(2.10) The solution for the minimum can be found by setting J Δ x 0 J Δ y - фото 23

The solution for the minimum can be found by setting ∂J /∂Δ x = 0 = ∂J /∂Δ y , then solving for Δ x and Δ y :

(2.11) 212 with solution 213 The solutio - фото 24

(2.12) with solution 213 The solution for Δ y may be found in similar fashion as - фото 25

with solution

(2.13) Global Navigation Satellite Systems Inertial Navigation and Integration - изображение 26

The solution for Δ y may be found in similar fashion as

(2.14) Global Navigation Satellite Systems Inertial Navigation and Integration - изображение 27

2.3.2.2 Navigation Solution Procedure

Transmitter positions x 1, y 1, x 2, y 2are known; the transmitter locations are typically provided by the GNSS satellites or known to be at a fixed surveyed location for a terrestrial source. Signal travel times Δ T 1, Δ T 2are measured by the system. Initially, the user positions Global Navigation Satellite Systems Inertial Navigation and Integration - изображение 28are assumed. Set position coordinates X , Y equal to their initial estimates:

Global Navigation Satellite Systems Inertial Navigation and Integration - изображение 29

Compute the range errors:

(2.15) 216 Compute the θ angle see Figure 23 217 - фото 30

(2.16) Compute the θ angle see Figure 23 217 Compute update to user position - фото 31

Compute the θ angle (see Figure 2.3):

(2.17) Compute update to user position 218 219 - фото 32

Compute update to user position:

(2.18) 219 Compute a new estimate of position using the update 220 - фото 33

(2.19) Compute a new estimate of position using the update 220 Continue to - фото 34

Compute a new estimate of position using the update:

(2.20) Global Navigation Satellite Systems Inertial Navigation and Integration - изображение 35

Continue to compute θ , Δ R 1, and Δ R 2from these equations with new values of Global Navigation Satellite Systems Inertial Navigation and Integration - изображение 36. Iterate Eqs. (2.15)– (2.20)and stop when Δ x and Δ y become less than the desired accuracy:

233 User Solution and Dilution of Precision DOP Just as in a landbased - фото 37

2.3.3 User Solution and Dilution of Precision (DOP)

Just as in a land‐based system, better accuracy is obtained by using reference points (i.e. ranging sources) well separated in space. For example, the range measurements made to four reference points clustered together will yield nearly equal values. Position calculations involve range differences, and where the ranges are nearly equal, small relative errors are greatly magnified in the difference. This effect, brought about as a result of satellite geometry, is known as dilution of precision (DOP). This means that range errors that occur from other independent effects such as multipath, atmospheric delays, and/or satellite clock errors are also magnified by the geometric effect.

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