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UAV See & Avoid Employing Vision Sensors
Aerospace Control and Guidance Systems Committee Meeting March 4, 2004 Eric Portilla Northrop Grumman Corporation Integrated Systems
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See and Avoid Role in Collision Avoidance
Collision Avoidance Has Many Layers of Protection See & Avoid Functionality Is Last Line of Defense All Preventative Measures Fail Procedural Air Traffic Management See & Avoid Sensors UAV Blurs Functional Boundary Source Of Data Not Important Work With Cooperative Sensors Procedural Cooperative Traffic Avoidance Air Traffic Management See & Avoid
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Manned Vehicles See & Avoid
Detection (See) Cooperative Situational Awareness Transponder Communication Position Broadcasts Ground Radar Uplink Pilot Eyes Verification of Situational Data Non-Cooperative Vehicles Avoidance Collision Avoidance Algorithms Pilot Intangibles Experience Reasoning Data Fusion and Evaluation
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Sensor Requirements Driven By Avoidance
Provide Sufficient Information Tracking Associate Current to Previous Data ID Data Correlation Path Projection Determine Threat Estimate Time To Collision Adequate Detection Range Time To Perform A Maneuver
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TCAS II Surveillance Leverage Aircraft Transponders
Interrogates on 1030 MHz Mode A/C All Calls & Mode S Replies On 1090 MHz Altitude ICAO Address (Mode S Only) Directional Antenna Relative Bearing (~ +/- 5°) Response Time Used To Calculate Range Certified and Mandated Required By FAA >30 Passengers >33,000 lbs Gross Take-Off Weight
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Automatic Dependent Surveillance – Broadcast (ADS-B)
Broadcasts Own GPS Position, Altitude Accurate ID Intent Range (>100 nmiles) Rebroadcasts Extend Range Three Datalinks Universal Access Transceiver Mode-S VDL – VHF (Asia and Europe Only) Requires Compatible Equipment Not Mandated Currently Not Widely Used
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ADS-B Conceptual Diagram
Class A Airspace Non-Transponder Equipped Aircraft Transponder Equipped Aircraft Transponder Equipped Aircraft Non-Transponder Equipped Aircraft Transponder Equipped Aircraft 100nmi
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Traffic Information System–Broadcast (TIS-B)
Ground-Based System Integrates Primary Radar (No Altitude) Transponder Returns Broadcasts Information ADS-B Format “Fills The Gap” For Situational Traffic Awareness Provides Non-Cooperative Radar Data Requires Ground Infrastructure Currently Only Pockets of Coverage Alaska Capstone Ohio Valley Prescott, Az Currently Only Transponder Data
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TIS-B Conceptual Diagram
Class A Airspace Transponder Equipped Aircraft 100nmi Non-Transponder Rebroadcast Equipment Primary Radar
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Radar Gimbaled or Electronically Scanned
Configured To Meet Field Of Regard Requirements Detected Threat Data Elevation Angle Bearing Angle Range Good Range ( nm for GA) All Weather No ID Requires Correlation Algorithm
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Vision Sensors Multiple Fixed Staring Sensors
Configured To Meet Field Of Regard Requirements On-board Image Processing for Moving Target Detection Detected Threat Data Elevation Angle Bearing Angle Image Size Does Not Provide Range
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Detection Techniques Detection
Own Vehicle Motion Causes Image Movement Creates An Optical Flow Field Intruder Aircraft Do Not Move With The Background Flow Field Discontinuity
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See & Avoid Sensing Architecture
TIS-B ADS-B TCAS Sensor Data Management and Correlation Establish Sensor Data Tracks Correlate Sensor Data to Intruders KF-based Optimal Tracking Filter One Filter for Each Intruder FDI Sensor Errors Sensor Correlation Errors Autonomous Avoidance Subject to Ownship Performance Limitations Radars Vision Sensors Reliable and Fault-Tolerant Detect, See, and Avoid (DSA) Capability Through Multiple Dissimilar Sensors Integration
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Combined Sensor Configuration
Benefits Realized Protection Against All Vehicles Coop & Non-Coop Multiple Dissimilar Sensors Redundancy Fault Tolerance Reliability Concept of Operations: Fuse Multi-Sensor Data Processing Track Reports: TCAS, ADS-B & TIS-B Can Be Correlated On-Board Provide Same ICAO Address ID’s Vision And Radar Require Track Correlation Sensor Level? System Level?
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Sensor Protection Volumes
TCAS/ADS-B/TIS-B: Cooperative, emission All aspect All weather Radar: Non-cooperative, emission Vision: Non-cooperative, no emission
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Fusion Architectures: “Pre-Detection Fusion”
Centralized Measurement Association and Tracking Sensor 1 Local Measurements Local Measurements Sensor 2 Global Tracks Sensor n Local Measurements Pros: Theoretically Optimal Topology Allows Optimal Associations More Accurate Global Tracks Cons: Computationally Prohibitive With Many Targets (NP Hard) High Integration Risk (Sensors with Different Sample Rates) Susceptible to Sensor Degradation; Difficult to Detect and Isolate Faults
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Fusion Architectures: “Post-Detection Fusion”
Sensor 1 Local Measurements Multi-Target Tracker Local Tracks Sensor 2 Local Measurements Multi-Target Tracker Local Tracks Correlated Association Track Track Fusion Local Tracks Global Tracks Sensor n Local Measurements Multi-Target Tracker Local Tracks Pros: Reduced Complexity for Local Multi-Target Trackers Improved Modularity & Easier Integration Improved Sensor Fault Detection & Isolation Capability Cons: Less Accurate Data Associations and Global Tracks
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Summary Equivalent Level Of Safety
Detect, Recognize, Decide, and Maneuver Perform at Least as Well as if a Human Pilot Was Onboard In Reality Much Higher Vision Sensors Easily Out Perform Pilot Eyes Data Processing Remains The Key! Multiple Sensors Create Robustness But Add Complexity Data Fusion Target Duplication False Targets Sensor Transitions
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