Signal Processing - September 2017 - 103

∆Vfront (m/s)

since it has the highest bound among the midlatitude GBAS
several airborne monitors designed to detect ionospheric spathreat models.
tial decorrelation that might be invisible or undetectable at the
The CONUS threat model can be used in other midlatitude
ground facility, instead of relying on PDGS with which the
regions with the condition that a penalty on
resulting system availability could be unacsystem availability caused by applying this
ceptably poor [36]. One monitor is the dual
This article aims to provide
more conservative threat model than actusmoothing ionosphere gradient monitor algoally required to the mitigation strategy of
a broad and comprehensive rithm (DSIGMA) that uses the difference
geometry screening (explained later in this
between 30-s and 100-s carrier-smoothed
understanding of
section) is not significant. However, it does
code measurements to detect the smoothing
ionospheric impacts on
not apply in equatorial regions since ionofilter lag difference caused by ionospheric
GNSS-based safety-critical
spheric gradients that greatly exceed its upper
gradients. Others methods include (but are
systems and up-tobound (425 mm/km) were observed in low
not limited to) an airborne CCD monitor
date signal processing
geomagnetic latitude locations. The Brazil
similar to that implemented in the ground
ionospheric study project, initiated in Octofacility and a simpler version of geometry
techniques for monitoring
ber 2013, discovered a significant number of
screening. In addition, the GAST-D ground
and mitigation of
extremely large gradients with magnitudes of
facility plans to add differential carrier
ionospheric anomalies.
approximately 400-850 mm/km [14]. Data
monitors, called ionosphere gradient monisets gathered from several Brazilian GNSS
tors (IGMs), over medium-to-long baselines
reference station networks were analyzed using the LTIAM
to make large gradients observable directly, without requiring
tool to obtain the result shown in Figure 4.
a significant change over time.
A number of states in the Asia-Pacific region are located in
Figure 5 shows the results from the worst-case ionospheric
or near the geomagnetic equator, but each state may be confrontanomaly impact simulation (which assumes the worst-case
ed with poor observability of anomalous gradients from GNSS
front-user-satellite geometry and timing) performed to assess
stations distributed in a small region. To solve this difficulty
the level of threat space mitigation for GAST-D GBAS. The
common to the states, the Communication, Navigation, and Surtwo-dimensional ionospheric threat space as a function of
veillance (CNS) subgroup of ICAO Asia-Pacific Air Navigation
gradient and relative front speed ^TV h was examined with
Planning and Implementation Regional Group (APANPIRG)
three monitors: ground CCD monitor, airborne DSIGMA, and
established the Ionospheric Studies Task Force (ISTF) in 2011 to
ground IGM. Ionospheric gradients that cause range errors
facilitate ionospheric data collection, sharing, and analysis. The
greater than 2.75 m (the maximum allowable range error)
data collected and shared by the member states were processed
using two methods, the LTIAM and the single-frequency carrier200
based and code-aided technique (SF-CBCA) [35]. The resulting
bound of the Asia-Pacific regional threat model was suggested by
150
the ISTF as presented in Figure 4.
Anomalous ionospheric spatial decorrelation within the
100
threat model should be detected and mitigated by GBAS integrity monitors. The CAT-I GBAS ground facility includes the
50
code-carrier divergence (CCD) monitor that detects ionospheric anomalies by observing the time-variation of divergence
0
between code and carrier measurements [36]. Thus, this monitor
is capable of detecting an ionospheric front with a relative speed
-50
^TV h with respect to the lines of sight from the ground facility
to satellites. A stationary front with respect to the ground (i.e.,
-100
300
350
400
450
500
550
relative speed zero) is the worst case from the point of view of
Gradient (mm/mm)
the GBAS ground facility because no changes in ionospheric
Detected by DSIGMA or Ground CCD
delay are observed by the CCD monitor. A simulation-based
(Pmd_DSIGMA)min < 10-9 or (Pmd_CCD)min <10-9
method called position-domain geometry screening (PDGS)
was developed to protect users from the threats that may not
Detected by IGM
(Pmd_IGM)min < 10-9
be detected by the ground facility [37], [38]. The mitigation
strategy of this method is to simulate worst-case ionospheric
Detected by DSIGMA and IGM
errors that a GBAS user might suffer based on the threat model
(Pmd_DSIGMA)min × (Pmd_IGM)min <10-9
and to reduce GBAS user position error to acceptable levels by
inflating one or more broadcast integrity parameters in near
FIGURE 5. The mitigation of anomalous ionospheric spatial decorrelation
real time.
using three monitors: ground IGM, airborne DSIGMA, and ground CCD
The GBAS Approach Service Type D (GAST-D) version of
monitor. Each region with a different color indicates the subset of threat
GBAS that will support Category II and III approaches utilizes
space mitigated by each monitor.
IEEE SIGNAL PROCESSING MAGAZINE

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September 2017

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103



Table of Contents for the Digital Edition of Signal Processing - September 2017

Signal Processing - September 2017 - Cover1
Signal Processing - September 2017 - Cover2
Signal Processing - September 2017 - 1
Signal Processing - September 2017 - 2
Signal Processing - September 2017 - 3
Signal Processing - September 2017 - 4
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Signal Processing - September 2017 - 6
Signal Processing - September 2017 - 7
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Signal Processing - September 2017 - 128
Signal Processing - September 2017 - 129
Signal Processing - September 2017 - 130
Signal Processing - September 2017 - 131
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Signal Processing - September 2017 - 133
Signal Processing - September 2017 - 134
Signal Processing - September 2017 - 135
Signal Processing - September 2017 - 136
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Signal Processing - September 2017 - 138
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Signal Processing - September 2017 - 140
Signal Processing - September 2017 - 141
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Signal Processing - September 2017 - 143
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Signal Processing - September 2017 - 145
Signal Processing - September 2017 - 146
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Signal Processing - September 2017 - 148
Signal Processing - September 2017 - 149
Signal Processing - September 2017 - 150
Signal Processing - September 2017 - 151
Signal Processing - September 2017 - 152
Signal Processing - September 2017 - 153
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Signal Processing - September 2017 - 156
Signal Processing - September 2017 - 157
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Signal Processing - September 2017 - 187
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Signal Processing - September 2017 - 189
Signal Processing - September 2017 - 190
Signal Processing - September 2017 - 191
Signal Processing - September 2017 - 192
Signal Processing - September 2017 - 193
Signal Processing - September 2017 - 194
Signal Processing - September 2017 - 195
Signal Processing - September 2017 - 196
Signal Processing - September 2017 - Cover3
Signal Processing - September 2017 - Cover4
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