Advanced NextGen Algorithms in ACES: DAC, CNS, . . . Frederick Wieland, Ph.D.

1. Advanced NextGen Algorithms in ACES:DAC, CNS, . . .Frederick Wieland, Ph.D.

2. Agenda • ACES Architecture • Communications, Navigation, and Surveillance • Dynamic Airspace Design Service (DADS) • Time-based Merging and Spacing • Separation Assurance Framework (SAF) • Multi-Aircraft Batch Simulation Tool Proprietary Intelligent Automation, Inc.

3. Intelligent Automation Inc. Company Overview • Founded in 1987 • Woman-owned small business • Headquartered in Rockville, MD • 120 Professional staff (~55% PhD) • $21.5M revenue for 2009 • Specializes in R&D Organization • Sensors Signals and Systems Division • Control and Signal Processing • Communications and Sensors • Robotics & Electromechanical Systems • Distributed Intelligent Systems Division • Multi Agent Systems • Networks and Security • Air Traffic Management • Education & Training Technology Division IAI Strengths • Sustained record of excellence • Strong qualifications in supporting large DOD and NASA programs through primes • Solid record of leveraging research in support of primes

4. ACES Architecture • ACES instantiates: • As many flight agents as required • 21 pairs of ARTCC TFM and ATC agents (per CONUS), + 1 international • Airport TFM/ATC (as required) • ARTCC TFM/ATC (as required) • One ATCSCC TFM agent • 18 AOC agents for traffic generation • Trajectory generator agent • Run Time: • ACES simulation run times: • 1x (~50k flights) • 1hr 30mins with 3 x 4quad 2.33Hz Intel (8GB memory) • 3x (~150k flights) • 6 hours Proprietary Intelligent Automation, Inc.

5. CNS in ACES • Developed in 2005-2009 by IAI • Communications: • 53 different types of messages modeled • Both voice (VHF) and datalink (VDL2) modeled • Voice/datalink can be specified by flight and/or by message type • Voice channels can be assigned by runway if desired • Navigation • VOR/DME is default • GPS also provided with three levels of accuracy: • Standard (default), WAAS for center enroute, LAAS for terminal • Can disable and get “true navigation” • Surveillance • SSR (1090 mode C) by default • ADSB mode S • All flights respond to SSR interrogation, flights configured with ADS-B will broadcast position • Assumption: SSR exists at each airport, tracon, and ARTCC Proprietary Intelligent Automation, Inc.

6. Comm Messages by Phase of Flight Transition Pushback/ Departure Enroute Arrival Landing Proprietary Intelligent Automation, Inc.

7. Voice and Ground Message Propagation Voice Voice failure modes: Step-on or weak signal If sender does not receive an ack within a specified time, message is retransmitted. Datalink Datalink failure modes: signal weak (dist between flight and VDL2 tower) or buffer overflow at VDL2 protocol layer. Message is retransmitted after a delay. If several attempts fail, message is transmitted via voice. Proprietary Intelligent Automation, Inc.

8. Some Comm Analysis Examples Voice Datalink Proprietary Intelligent Automation, Inc.

9. Navigation • VOR/DME or GPS selectable by aircraft type—AOC pair. • VOR/DME locations input via data file (standard file with all VOR locations provided) • GPS with three levels of accuracy: standard, LAAS, WAAS • Standard: ± 3.15 meters long/lat, ± 4.75 meters altitude • LAAS: ± 3.10 meters long/lat, ± 1.0 meters altitude • WAAS: ± 0.91 meters long/lat, ± 1.07 meters altitude • Flight control can be driven by raw measurements or by estimated state measurements from navigation • 3 DOF linear and Kalman Filter model based upon position and speed measurements Proprietary Intelligent Automation, Inc.

10. Effect of Navigation Feedback Loop Proprietary Intelligent Automation, Inc.

11. Surveillance • SSR-mode C, inaccuracies arise from barometric altimeter measurements • Mode S contains radio parameters (power/setting/gain) and ground station locations • Possible effects on ACES when aircraft control is derived from internal navigation (and thus surveillance is imprecise) is as follows: • ARTCC ATC attempts to resolve a conflict when none exists • ARTCC ATC does not resolve a conflict when one exists • ARTCC ATC delays in responding to a conflict situation Proprietary Intelligent Automation, Inc.

12. Causal Attribution Metric (CAM) • NEED • The CAM Task was created to allow the ACES System to record flight delays and the causes of those delays. • Flight Delay is recorded from two perspectives: • From the flight’s perspective, the actual amount of delay and where the delay was experienced is recorded • From the ATC and TFM ground models perspective, insight about why the delay was applied is recorded • KEY COMPONENTS OF THE APPROACH TAKEN • Two classes were created as containers to hold the delay data – one class for the flight’s perspective and one for the ATC and TFM ground model’s perspective • The fields of the two classes were recorded to the database using the ACES Local Data Collection (LDC) subsystem • By processing the LDC output data of the two classes, it is possible to capture which flight was delayed, the amount of the delay, the source/cause of the delay, as well as other pertinent information. • Key recorded field values from the ATC and TFM ground model’s perspective include flight ID, facility ID, facility name, facility type, simulation time, category, description, requested crossing time, requested delay • Key recorded field values from the flight model’s perspective include flight ID, ETMS flight ID, airline flight number, facility type, facility index, facility name, scheduled entry time, actual entry time, delay • IDENTIFIED SOURCES OF FLIGHT DELAY • En Route sector congestion • Spacing at the arrival fix • Departure and arrival spacing in the TRACON • Conflict Detection and Resolution (CD&R) • Capacity restriction at nodal airports (AAR, ADR) • Aircraft spacing at runway modeled airport • AOC operations • Surface Traffic Limitations (STL) • Surface Traffic Limitations Enhancement (STLE) • Scenario events • Reroutes due to constrained airspace • Traffic Management Advisor (TMA) • Dynamic Airspace Reconfiguration (DAC) • CD&R by Handoff Protocol Proprietary, Intelligent Automation, Inc.

13. DADS (Dynamic Airspace Design Service) • NEED • DAC (Dynamic Airspace Configuration) algorithms are used to reconfigure airspaces. ACES should be able to call any DAC algorithm during a simulation and re-load the airspace boundary and configuration information. • The DAC algorithm may be treated as a black box running on any machine in a heterogeneous network. • The architecture should allow the plug and play of existing (DAU and Voronoi) and future DAC algorithms. • KEY COMPONENTS OF THE APPROACH TAKEN • A socket based client-server architecture is used. Sockets facilitate communication over a LAN with diverse hardware (Unix, Windows etc.) • The DADS socket client is an ACES plug-in and can be configured from within the ACES GUI. • The ACES agent based architecture is used to pause a simulation, trigger a call to DADS-DAC, re-initialize airspace boundaries in ACES and resume simulation. • The deployment parameters for the DAC (IP, ports etc) can be configured by the ACES researchers. • The parameters for the DAC can be read from a configuration file and the plug-in GUI generates the input fields. • The ACES researcher can enter the time relative to the simulation start at which the DAC is called. • DADS takes care of all the file format conversions from ACES to ETMS and back at runtime. • A DADS-DAC ICD (Interface Control Document) has been released to facilitate plug and play of DAC algorithms. Proprietary, Intelligent Automation, Inc.

14. Dynamic Airspace Units (DAUs) : Automatic Slicing Algorithm • OBJECTIVES • Design algorithms that can automatically adjust airspace boundaries depending on traffic demand patterns / weather • Facilitate integration into NASA modular simulation environment • Develop workload proxy metrics for use with DAC- such as Simplified Dynamic Density (SDD). • Facilitate what-if analyses of large scale airspaces and fast-time simulations • Develop methods for benefit analysis of NextGen technologies and new airspace classes (including DAC) Horizontal Slicing Concept Find common boundary portion and cut off slices of specified width, parallel to common boundary “trend” line • KEY COMPONENTS OF THE APPROACH TAKEN • Design algorithms that can automatically create shareable Dynamic Airspace Units (DAUs) near common sector boundaries (“slicing”) • Uses both horizontal and vertical slicing, user-specified extent (depth) of boundary adjustments and able to utilize various workload proxy metrics such as Sector Occupancy counts and SDD • At every time step, check workload proxy metrics (e.g. SDD). If metric in a sector exceeds overload threshold, attempt to change boundary between this sector and neighbor(s) • Cut progressively larger slices off this sector, stop when workload proxy metric is pushed below overload limit. • Abide by additional conditions such as, continuity of airspace boundaries (no drastic changes) and limit DAC to within Area-of-Specialization or own Center Horizontal and Vertical Slicing Source: Dynamic Airspace Reconfiguration (Algorithms and Metrics Facilitating NextGen Airspace Analysis) by Alexander Klein – ATA, Inc , Mark D. Rodgers, Panta Lucic – CSSI, Inc, Ken Leiden – Mosaic ATM, Inc, Steve Peters – Alion Science, Inc Proprietary, Intelligent Automation, Inc.

15. Cutting-Edge Research in DAC/TFM • Input flights, output NAS configuration (flights with sectorization, capacities, flow restrictions) • What are the details of this process? • Does it converge? • How often is this loop evaluated (15 mins, 30, 1 hour. . .) • Is the process implementable in the far-term? Predicted Trajectories New Sectors & Capacities Dynamic Airspace Configuration Traffic Flow Management Flights & Weather Trajectory Generation Predicted Trajectories NAS Configuration New Sectors & Capacities with Flow Restrictions Flow Restrictions Proprietary Intelligent Automation, Inc.

16. Time Based Merging and Spacing Testbed Objectives • A simulation testbed is required to assess various new NextGen concepts that are aimed at addressing the ever-increasing number of aircraft in the NAS • Airborne merging and spacing (M&S): a concept involving the delegation of separation assurance to the flight deck while still maintaining or exceeding current throughputs at the airports • Separation assurance is also needed for those aircraft that are not participating in M&S Results to Date Integrated merging and spacing speed algorithm (a modification of NASA’s AMSTAR algorithm) into ACES 6.2+ Developed concept of region of control and an accompanying route scheduler; integrated into ACES 6.2+ Enhanced IAI’s Kinematic Trajectory Generator (KTG) to handle M&S instructions and to fly four-dimensional trajectories (4DTs) Proprietary Intelligent Automation, Inc.

17. SAF(Separation Assurance Framework) • NEED • With many different ways of handling and studying the problem of separation assurance (SA), ACES users need the ability to rapidly configure the SA parameters used in simulations, including the selection of models for airspace regions, surveillance, conflict detection and resolution (CD&R), and the translation of conflict resolutions into executable maneuvers. • Because ACES users will want to study more and more ways of handling SA as time goes on, there is a need for a flexible framework underpinning the SA architecture that allows for the easy incorporation of new models. • KEY COMPONENTS OF THE APPROACH TAKEN • The framework depends on a set of newly developed ACES core enhancements that are needed by SA but are too general to be included in the SA plug-in. These include services that provide ground truth aircraft state data, surveillance data, conflict detection results, predicted flight trajectories, weather data, and meter fix crossings and a service that accepts instructions to maneuver aircraft. • SA models, including models of CD&R and of SA controllers, are added to ACES per configuration from a newly developed ACES plug-in called SeparationAssurance. These models implement different interfaces defined in the SA framework, allowing them to communicate with each other as well as with other parts of the ACES system without needing to know how this communication is handled. • Uses the Spring framework to load SA configuration objects into a simulation from an XML file specified in the SeparationAssurance plug-in configuration GUI. • An SA Configuration Editor program provides a GUI for editing new and existing SA configurations. Proprietary, Intelligent Automation, Inc.

18. Multi Aircraft Batch Simulation ToolProject Type: CONITS Task Order; Project # 777; Project Code: Multi Aircraft Batch Tool • NEED • NASA Langley’s ATOS laboratory requires an external module that will serve multiple purposes: (1) generate the background air traffic for pilots participating in ATOS HITL experiments; (2) interact with ATOS as if the computer-generated traffic were operating as a piloted vehicle; and (3) serve as a stand-alone analysis testbed • KEY COMPONENTS OF THE PROPOSED APPROACH • Develop a “multi-aircraft batch simulation tool” that will serve as a fast time analysis product or a real-time product integrated with ATOS that will provide target generation for aircraft in all phases of flight including surface vehicle movement, incorporate NextGen air traffic management algorithms, include air-air as well as air-ground comm, display results graphically, incorporate wind and weather, and include visualization effects. • DELIVERABLES • A product that fully integrates with ATOS over the web using HLA that also could be used as a stand-alone product. • TEAM : • Raytheon Corporation (Prime Contractor—POC Pierre Beaudoin) • Intelligent Automation, Inc. (subcontractor) • VALUE TO THE CUSTOMER /TRANSITION CUSTOMER • Expected elimination of dependency on a product that is currently sourced from another air traffic control organization in Europe • NASA/Langley gains ownership and control of a product that will enhance the capabilities of ATOS Proprietary, Intelligent Automation, Inc.

19. In Summary. . . • ACES has become an extensible architecture for ATM algorithm proof-of-concept • Plug-ins for trajectory generators, DAC, SA • ACES has been used for benefits analysis • JPDO IPSA, New Vehicle NRAs • There are algorithms in ACES that do not exist anywhere else • CNS • It will continue to be used by NASA researchers for far-term studies, but is also useful for near-term studies • Delegated separation, data communications. . . Proprietary Intelligent Automation, Inc.