Global Positioning System Reference
In-Depth Information
Core performance
paradigm
Methodology Employed by:
GPS developers
GPS operations
Major assessment factors
SIS URE -Satellite
clock and ephemeris
errors, curve fit error,
group delay
Signal RF
characteristics -
spectrum, power,
modulation, coherency
Constellation design -
satellite placement,
orbit slot dynamics
Satellite reliability,
maintainability,
availability (to include
failure behaviors)
Basic user satellite
selection logic
Nominal receiver
tracking performance
Regional performance
paradigm
Methodology employed by:
GPS application-specific
user groups (aviation,
maritime, etc.)
Military mission planners
Major assessment factors
PPS vs. SPS signal
tracking
Single vs. dual
frequency
Space weather
behaviors
Regional behaviors of
Earth's magnetic field
Regional atmospheric
conditions and
associated receiver
troposphere model
errors
Projected regional
electromagnetic
interference
environment
Local performance
paradigm
Methodology employed by:
Individual GPS high-precision
users (surveyors, etc.)
After-the-fact mission assessment
Major assessment factors
Specific receiver design—
antenna type/pattern, signal A/D,
tracking loop design, satellite
selection criteria, PNT solution
Receiver integration—antenna
placement, effects on tracking
loops of INS coupling, effects on
PNT solution of various sensor
integrations
Local obscura/terrain/canopy
Local electromagnetic interference
source characteristics (placement,
power, antenna type/orientation,
spectrum, waveform)
Local atmospheric conditions,
localized space weather effects
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Usage scenario—platform
dynamics, antenna orientation
Figure 7.30
Framework for establishing application-specific methods to assess GPS performance.
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Local performance paradigm:
focused on an individual user application and
usage scenario within a local environment, provides the highest correlation to
individual user performance.
The techniques and procedures for assessing GPS performance on a global basis
cover an extensive list of topics. We discuss here two specific topics that are key in
the support of the performance assessment framework and can provide the reader
with a perspective on the assessment process:
•
Compensating for a relatively low density of global performance measure-
ments;
Estimating the effect of global ionosphere effects on single frequency perfor-
mance.
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The method for complementing direct performance measurements for global
performance assessments requires the generation of PPS estimated range deviations
(ERDs), the CS term for estimated SIS UREs. ERDs are very flexible in that they may
be computed for any location within view of the GPS satellite of interest. ERDs are
limited in that they are based on the difference between the CS Kalman Filter's cur-
rent state estimate and the predicted state at the time of upload. CS ERDs tend to be
optimistic in their representation of the PPS SIS URE, because they do not include
curve fit error, and a portion of the ephemeris error tends to be unobservable within
the current CS architecture. These limitations in the ERD computation result in an
estimate that is generally 10% to 20% below the “true” SIS PPS URE within the cur-
rent GPS program. This optimism is mitigated somewhat in practice by comparing
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