Targeted Ephemeris Decorrelation Parameter Inflation for Improved LAAS Availability during Severe Ionosphere Anomalies

1
Targeted Ephemeris Decorrelation Parameter
Inflation for Improved LAAS Availability during
Severe Ionosphere Anomalies

• Shankar Ramakrishnan, Jiyun Lee, Sam Pullen and
  Per Enge
• Stanford University

ION National Technical Meeting - 2008 San Diego,
California Session A2 Algorithms Methods 1
January 28, 2008
2
Ice Breaker

• Flight Delayed or Cancelled due to Bad Weather?
• Flight Diverted due to Poor Runway Visibility?
• What is it like to land without Runway
  Visibility?

Video Courtesy http//youtube.com/watch?vuigpqpD
WIwE Image Courtesy www.images.google.com
3
Overview

• Current Autoland systems based on Instrument
  Landing Systems
• ILS based systems have inherent limitations
• Next-Gen Air Traffic Systems to extensively
  leverage GNSS technology
• Local Area Augmentation System (LAAS) to
  eventually provide autoland capability
• LAAS systems must meet stringent requirements on
  four key system parameters
• Accuracy
• Integrity
• Continuity
• Availability

4
Integrity Requirement

• Federal Aviation Administration (FAA) places
  strict requirements on risk of missing touchdown
  box 10-9 per approach
• Flight Technical Error (FTE)
• Navigation Sensor Error (NSE)

5
Integrity Requirement

• Federal Aviation Administration (FAA) places
  strict requirements on risk of missing touchdown
  box 10-9 per approach
• Flight Technical Error (FTE)
• Navigation Sensor Error (NSE)

Alert Limit
6
GPS Error Sources
GPS clock error Ephemeris error
Ionospheric delay
Tropospheric delay
Multipath error
Receiver noise
7
Error Mitigation Differential GPS (DGPS)
GPS clock error Ephemeris error
Ionospheric delay
Tropospheric delay
Differential Corrections
Receiver noise
Multipath error
8
Local Area Augmentation System (LAAS)
Space Segment
Ranging Signal Orbit parameters
LAAS Ground Facility (LGF)
Airborne User
1) Differential corrections
2) Detect failure and Alarm user
3) Integrity parameters
VHF Data Broadcast
Multiple Receivers
9
User Error Bound

• LAAS Provides Protection Level that Bound
  Residual User Errors out to Integrity Requirement
• Measurement Noise (air, ground)
• Nominal Ionosphere Decorrelation
• Multipath
• Undetected Faults

Protection Level
Alert Limit
10
Ionosphere Something to Fear About

• Ionosphere Anomalies poses the biggest integrity
  threat to LAAS
• Periods of Solar High results in anomalous
  ionospheric conditions.
• Users can suffer errors as high as 50m just due
  to the ionosphere!
• Efficient algorithms required at the LAAS ground
  facility to detect and mitigate such risks

Image Courtesy http//sohowww.nascom.nasa.gov/gal
lery/SolarCorona/combo001.html
11
Modeling an Ionosphere Front
Simplified Ionosphere Wave Front Model a wave
front ramp defined by the slope. width and
the front speed
Front Speed
Front Slope
LGF IPP Speed
Front Width
Airplane Speed
LAAS Ground Facility
Data from Past Solar Storms analyzed to determine
upper and lower bounds for the three parameters.
12
Ionosphere Induced Range Error

• Ionosphere Threat Model Basis for Worst-case
  airborne differential range errors
• Use of a Code-Carrier Divergence Rate (CCD)
  Monitor limits impact.
• Closed Form Range Error Tables derived which
  leverage front velocity as key parameter
• Slow Front Speed 10m/s lt ?v lt 40m/s
• No CCD Detection
• Largest Error
• Moderate Speed Monitor Starts to Trip, Errors
  Drop
• Fast Speed Monitor Trips for sure
• Obtain Maximum Ionosphere Induced Error in Range
  (MIER)

13
Meeting LAAS Integrity
Under Faulted Conditions Position Error lt Total
Error Limit
Total Error Limit
Position Error
Under Nominal Conditions Protection Level lt
Alert Limit
Alert Limit
Protection Level
14
Position-Domain Geometry Screening

• Worst Case Any two satellites in a geometry can
  be impacted simultaneously.
• Require Error In Vertical!
• Position domain verification is needed to
  establish the safety of a given geometry
• Max. Iono. Error Vertical (MIEV) is compared to
  Obstacle Clearance Surface (OCS) limit to
  determine if a given user subset geometry is
  safe
• If MIEV falls below OCS, no hazard would occur
• If MIEV exceeds OCS, geometry is potentially
  hazardous

Need an Efficient Algorithm to Eliminate Unsafe
Subsets
15
Protection Level and Sigmas
Vertical navigation error bound evaluated by
aircraft
Standard deviation of differentially corrected
pseudorange error
Vertical Protection Level (VPL)
Broadcast Integrity Parameters
LGF
16
Real-Time P-Value Inflation Step 1
Increase P-value by a small amount DP on all
approved satellites and re-evaluate availability
of remaining unsafe subsets at all separations
from DH. Continue until no unsafe subsets remain
or until PA is reached.
PA
Many small steps DP
Pnom
1
2
3
4
N
unsafe subsets
Satellites Approved by LGF
17
Real-Time P-Value Inflation Step 2
Increase P-value of one approved satellite by DP
and re-evaluate availability. Continue until no
unsafe subsets remain or until PB is reached. If
PB is reached first, repeat as needed with 2nd
satellite, then 3rd satellite, etc. until all
satellites reach PB.
PB
Current heuristic to select SV to inflate
Maxi Sverti (worst subset) / Sverti (all
usable)
DP
PA
Pnom
1
2
3
4
N
unsafe subsets
Satellites Approved by LGF
Pnorm 135e-6 PA 170e6
PB 270e-6
18
Real-Time P-Value Inflation Step 3
If PB is reached for all satellites while unsafe
subsets remain, revert to increasing P-values on
all satellites until no unsafe subsets remain
available (at any separation from DH).
DP
PB
PA
Pnom
unsafe subsets
1
2
3
4
N
Satellites Approved by LGF
19
Pseudocode for Targeted P-value Inflation
Begin Execution Compute Inflated spr_gnd to
protect DH 2km. Input for subsequent DH
distances. For DH 36 km For Distance
DH,DH1,DH2,DH3,DH7 Determine Unsafe
Subsets While Exists(Unsafe
Subsets) P-value
PvalueInflation(DH,Distance,P-value)
Broadcast Inflated P-values, spr_gnd for N
all-in-view satellites LGF can track End
Execution
20
Results Memphis Intl. Airport
21
Results Memphis Intl. Airport
22
Results Memphis Intl. Airport
23
Results Memphis Intl. Airport
24
Results Memphis Intl. Airport
25
Results Memphis Intl. Airport
26
Results Memphis Intl. Airport
27
Results Memphis Intl. Airport
28
Results Major US Airports
Airport RTCA 24 DH6km RTCA 24 DH5 km RTCA24 DH4 km RTCA 24 DH3 km RTCA 24 DH2 km RTCA 24 DH1 km
Memphis (MEM) 1.000 1.000 1.000 1.000 1.000 1.000
Denver (DEN) 1.000 1.000 1.000 1.000 1.000 1.000
Dallas (DFW) 1.000 1.000 1.000 1.000 1.000 1.000
Newark (EWR) 1.000 1.000 1.000 1.000 1.000 1.000
Washington (DCA) 0.993 0.997 1.000 1.000 1.000 1.000
Los Angeles (LAX) 1.000 1.000 1.000 1.000 1.000 1.000
Orlando (MCI) 0.990 1.000 1.000 1.000 1.000 1.000
Minneapolis (MSP) 1.000 1.000 1.000 1.000 1.000 1.000
Chicago (ORD) 0.999 1.000 1.000 1.000 1.000 1.000
Seattle (SEA) 1.000 1.000 1.000 1.000 1.000 1.000
29
Summary

• Targeted Ephemeris Decorrelation Parameter
  Inflation Algorithm helps meet integrity.
• Achieves guaranteed LAAS Cat I availability for
  major US airports
• Computationally robust
• Average Computation Time 30 seconds per epoch
• Worst Case Computation Time 73 seconds per
  epoch
• Computations performed on Matlab running on a
  Intel Core 2 Duo 2.2 Ghz Processor.
• Scope for further optimization of performance
• Algorithm scalable to changes in satellite
  constellation.

30
Acknowledgement This work was supported by the
affiliated members of the Stanford Center for
Position Navigation and Time (SCPNT)
Question Time
31
Backup
32
Outline

• Overview of Problem
• Updated Ionosphere Threat Model
• Ionospheric Anomaly Induced Range Error
  Computation
• Position-Domain Geometry Screening
• Proposed Algorithm for Geometry Screening
• Results Conclusion