Advanced NextGen Algorithms in ACES:
DAC, CNS, . . .
Frederick Wieland, Ph.D.
Intelligent Automation, Inc.
15400 Calhoun Drive, Suite 400
Rockville, MD 20855
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
5/28/2016
Proprietary Intelligent Automation, Inc.
2
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
3
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
5/28/2016
Proprietary Intelligent Automation, Inc.
4
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
5/28/2016
Proprietary Intelligent Automation, Inc.
5
Comm Messages by Phase of Flight
Transition
Pushback/
Departure
Enroute
Arrival
Landing
5/28/2016
Proprietary Intelligent Automation, Inc.
6
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.
5/28/2016
Proprietary Intelligent Automation, Inc.
7
Some Comm Analysis Examples
Voice
Datalink
5/28/2016
Proprietary Intelligent Automation, Inc.
8
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
5/28/2016
Proprietary Intelligent Automation, Inc.
9
Effect of Navigation Feedback Loop
5/28/2016
Proprietary Intelligent Automation, Inc.
10
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:
•
•
•
5/28/2016
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.
11
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
12
Flight 44 (AAL28) Delay From SFO To LAX
Total Delay
TRACON Spacing - Flight 44
Spaced Behind Flight 95
Takeoff Delay To Meet ADR
Gate Departure Delay - Congestion
In Sector ZLA13
0
5
10
15
20
25
30
35
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.
5/28
/201
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.
13
5/28/2016
ACES + DADS Fetch Data
DADS Socket
DADS Service Invoke
Set Data
plug-in
Client
Read
Write
DADS Socket
Server
Invoke
Invoke
DADS GUI
Algorithm
Proprietary, Intelligent Automation, Inc.
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
Sector B
Sector A
B_CORE
A_CORE
Common boundary “trend” line
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 AreaSource:
Dynamic Airspace
(Algorithms and Metrics Facilitating NextGen Airspace Analysis) by Alexander Klein –
of-Specialization
or own Reconfiguration
Center
ATA, Inc , Mark D. Rodgers, Panta Lucic – CSSI, Inc, Ken Leiden – Mosaic ATM, Inc, Steve Peters – Alion Science, Inc
Horizontal and Vertical Slicing
14
5/28/2016
Proprietary, Intelligent Automation, Inc.
Cutting-Edge Research in DAC/TFM
Predicted
Trajectories
Flights &
Weather
Trajectory
Generation
Predicted
Trajectories
Dynamic
Airspace
Configuration
New Sectors & Capacities with Flow Restrictions
New
Sectors &
Capacities
Traffic
Flow
Management
NAS
Configuration
Flow Restrictions
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?
5/28/2016
Proprietary Intelligent Automation, Inc.
15
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.
16
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.
17
5/28/2016
Proprietary, Intelligent Automation, Inc.
Multi Aircraft Batch Simulation Tool
Project 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 computergenerated 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.
Agents dynamically exchange operational information
AOC
ATCSCC TFM
Congestion Alerts, GDP/GSP Msgs
ARTCC TFM
TRACON TFM
Airport TFM
TFM Restriction Msgs
ARTCC ATC
TRACON ATC
Airport ATC
ATC Clearance Msgs
Flight
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
18
5/28/2016
Proprietary, Intelligent Automation, Inc.
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 farterm studies, but is also useful for near-term studies
– Delegated separation, data communications. . .
5/28/2016
Proprietary Intelligent Automation, Inc.
19
Intelligent Automation, Inc.
Innovative solutions to meet your
technical challenges …..
15400 Calhoun Drive, Suite 400
Rockville MD, 20855
(301) 294-5200
i-a-i.com