The Wide Area Augmentation System

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1 The Wide Area Augmentation System Stanford University

2 What is Augmentation? 2 Add to GNSS to Enhance Service Improve integrity via real time monitoring Improve availability and continuity Improve accuracy via corrections Space Based Augmentations (SBAS) e. g. WAAS, EGNOS, MSAS, GAGAN Ground Based Augmentations(GBAS) e. g. LAAS Aircraft Based Augmentations (ABAS) e. g. RAIM, Inertials, Baro Altimeter

3 Why Augmentation? 3 Current GPS and GLONASS Constellations Cannot Support Requirements For All Phases of Flight Integrity is Not Guaranteed Not all satellites are monitored at all times Time-to-alarm is from minutes to hours No indication of quality of service Accuracy is Not Sufficient Even with SA off, vertical accuracy > 4 m Availability and Continuity Must Meet Requirements

4 Vertical Guidance Benefit: Lower DH CAT III 0-50 ft DH CAT II 100 ft DH CAT I 200 ft DH GLS 250 ft DH L-NAV V-NAV 350 ft DH NPA Courtesy: Sherman Lo 4 Requirement: More Accuracy, Tighter Bounds DH = Decision Height

5 200 DH Requirements 5 Accuracy: < 4 m 95% Horizontal and Vertical Integrity: Less than 10-7 probability of true error larger than 40 m horizontally or 35 m vertically 6 second time-to-alert Continuity: < 10-5 Chance of Aborting a Procedure Once It Is Initiated Availability: > 99% of Time

6 WAAS Concept 6

7 WAAS Architecture 38 Reference Stations 3 Master Stations 4 Ground Earth Stations 2 Geostationary Satellite Links 2 Operational Control Centers Wide Area Augmentation System (WAAS) Program Status Courtesy: Federal Aviation Administration 7

8 Geostationary Satellites (GEO) PanAmSat 133 W Telesat 107 W Provides Dual Coverage Over United States Wide Area Augmentation System (WAAS) Program Status Courtesy: Federal Aviation Administration 8

9 Localizer Performance Vertical (LPV) Coverage Wide Area Augmentation System (WAAS) Program Status Courtesy: Federal Aviation Administration 9

10 WAAS RNP 0.3 Current Coverage Wide Area Augmentation System (WAAS) Program Status Courtesy: Federal Aviation Administration 10

11 Summary of Key WIPP Integrity Lessons Integrity Requirement of 10-7 Applies to Each and Every Approach Threat Models Required to Judge System Performance and Safety System Must Be Proven Safe Rationale/evidence for safety claim 11 Small Probabilities Are Not Intuitive

12 Overall Integrity Approach 12 Conventional Differential GPS Systems Rely on Lack of Disproof I ve been using it for N years and I ve never had a problem 10-7 Integrity Requires Active Proof Analysis, Simulation, and Data Must Each Support Each Other None sufficient by themselves Clear Documentation of Safety Rationale is Essential

13 Interpretation of Probability of HMI < 10-7 Per Approach Possible Interpretations Ensemble Average of All Approaches Over Space and Time Ensemble Average of All Approaches Over Time for the Worst Location Previous Plus No Discernable Pattern (Rare & No Correlation With User Behavior) Worst Time and Location 13

14 Probability of Integrity Failure Average Risk all conditions P( fault condition) P(condition) Specific Risk P( fault condition) 14

15 Probability of Being Struck by Lightning From the Lightning Safety Institute USA population = 280,000, lightning victims/year/average Odds = 1 : 280,000 of being struck by lightning Not everyone has the same risk One person struck 7 times 15 Naïve calculation: < 1e-38 probability

16 WAAS Interpretation Events handled case by case Events that are rare and random may take advantage of an a priori Deterministic events must be monitored or treated as worst-case Events that are observable must be detected (if risk > 10-7 ) Must account for worst credible undetected events 16

17 Error Distribution Distribution of errors may be formed over many conditions Leads to fat tails Need to characterize errors for worst allowable condition Not all conditions known or recognized Focus on the tail behavior as opposed to the core of the distribution For WAAS, nominal pseudorange errors are ~3 times smaller than implied by bound Position domain errors are more than 5 time smaller 17

18 Error Sources 18 Satellite errors Ephemeris Clock Signal Propagation errors Ionosphere Troposphere Local Errors Multipath Receiver Noise

19 Errors on the Signal Space Segment Errors Clock errors Ephemeris errors Propagation Errors Ionospheric delay Tropospheric delay Local Errors Multipath Receiver noise Common Mode Strong Spatial Correlation Weak Spatial Correlation No Spatial Correlation 19

20 Broadcast Orbit Errors 20

21 Orbit Estimation Techniques Common Measurement Process Geometric Observations 21 Kinematically Smoothed Geometric - Filtered instantaneous snapshot estimate Dynamic - model forces acting on the satellite

22 Errors Below the Detection Threshold 22

23 Ionospheric Effects 23

24 Ionospheric Delay 24

25 Seasonal Variations Courtesy: Pat Doherty & Jack Klobuchar 25

26 11-Year Solar Cycles 26

27 Thin Shell Model 27

28 Obliquity Factor 28

29 Correlation Estimation Process 29

30 Ionospheric Decorrelation About a Planar Fit (1 st Order) 30

31 Planar Fit to Local IPPs 31

32 Nominal ionosphere - Grid 32

33 Disturbed Ionosphere - IPPs 33

34 Failure of Thin Shell Model 34 Quiet Day Disturbed Day

35 Irregularity Definition 35 Conventional Definitions Tied to Physical Cause Storm, SED, TID, etc. Vertical Ionospheric Behavior That is Not Well Modeled Locally by a Plane Estimation is incorrect (biased) Difficulty using MOPS grid User errors may be larger than predicted by planar model Obliquity factor may be in error GIVE not guaranteed to bound

36 Threat Model 36

37 Worst Undetectable Distribution 37

38 11/20/ :00:00 GMT 38

39 Threats at the Edge of Coverage Courtesy: Seebany Datta-Barua 39

40 Edge of Coverage 2 Courtesy: Seebany Datta-Barua 40

41 Undersampling Within CONUS Courtesy: Seebany Datta-Barua 41

42 Small-scale Irregularity Courtesy: Seebany Datta-Barua 42

43 Artificial Undersampled Scenario Courtesy: Seebany Datta-Barua 43

44 WAAS Measurements Courtesy: Seebany Datta-Barua 44

45 Artificial WAAS Undersampling Scenario Courtesy: Seebany Datta-Barua 45

46 Real Undersampled Condition Courtesy: Seebany Datta-Barua 46

47 WAAS Measurements Courtesy: Seebany Datta-Barua 47

48 Conclusions WAAS is Fully Operational Excellent accuracy and availability 48 Probability of HMI Applies to Worst Predictable Condition Threat Models Essential for Validation Errors Below Detection Threshold Must Be Treated Conservatively Approach is Very Conservative Improves as our knowledge increases

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