American Institute of Aeronautics and Astronautics1

Evaluation of Separation Management Algorithmsin Class G Airspace

Richard Baumeister* and Regina Estkowski†

The Boeing Company, PO Box 3707, Seattle, WA. 98124

Graham T. Spence‡

The University of Sheffield, Sheffield, S1 3JD, UK

and

Reece Clothier§

ARCAA, GPO Box 2434, Brisbane Qld 4001, Australia

Use of Unmanned Aerial Vehicles (UAVs) in support of government applications hasalready seen significant growth and the potential for use of UAVs in commercial applicationsis expected to rapidly expand in the near future. However, the issue remains on how suchautomated or operator-controlled aircraft can be safely integrated into current airspace. Ifthe goal of integration is to be realized, issues regarding safe separation in densely populatedairspace must be investigated. This paper investigates automated separation managementconcepts in uncontrolled airspace that may help prepare for an expected growth of UAVs inClass G airspace. Not only are such investigations helpful for the UAV integration issue, theautomated separation management concepts investigated by the authors can also be usefulfor the development of new or improved Air Traffic Control services in remote regionswithout any existing infrastructure. The paper will also provide an overview of the SmartSkies program and discuss the corresponding Smart Skies research and development effortto evaluate aircraft separation management algorithms using simulations involving real-world data communication channels, and verified against actual flight trials. This paperpresents results from a unique flight test concept that uses real-time flight test data fromAustralia over existing commercial communication channels to a control center in Seattle forreal-time separation management of actual and simulated aircraft. The paper also assessesthe performance of an automated aircraft separation manager.

I. Introductionncreased automation of the Air Traffic Control (ATC) separation assurance process aims to reduce the workloadof air traffic controllers and potentially allow an increase of air traffic densities of manned and unmanned

platforms while maintaining current safety levels. Boeing Research and Technology (BR&T) has developed an ATCarchitecture, called the Automated Dynamic Airspace Controller (ADAC). The ADAC allows prototyping andtesting of the Separation Assurance function and can be used to evaluate different Separation Management (SM)approaches. Our scope is currently limited to Separation Assurance in uncontrolled Class G airspace with the ADACexercising centralized control over cooperative manned and unmanned aircraft. This approach will allow control ofcooperative manned and unmanned platforms from any global location with access to the Internet, in what waspreviously uncontrolled airspace. The ADAC can also support a decentralized and distributed separation assurancefunction where the currently centralized functions are executed onboard the aircraft. Such a decentralized structureis an area of ongoing research at BR&T.

* Research Engineer, Boeing Research and Technology , PO Box 3707, Seattle, WA† Research Engineer, Boeing Research and Technology , PO Box 3707, Seattle, WA‡ Research Associate, Automatic Control and Systems Engineering, Mappin Street.§ Smart Skies Project Manager, ARCAA.

I

AIAA Modeling and Simulation Technologies Conference10 - 13 August 2009, Chicago, Illinois

AIAA 2009-6126

Copyright © 2009 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

American Institute of Aeronautics and Astronautics2

The backbone of the ADAC architecture is a communications capability linking the centralized controller to theaircraft within the region being controlled. This communication capability is referred to as a Common InformationNetwork (CIN). Cooperative aircraft continually transmit current aircraft state information (akin to ADS-B1) to theADAC and are capable of receiving control commands from the ADAC via the CIN. Ancillary data sources, such asradar feeds necessary to track uncooperative aircraft, can also provide input to the ADAC using the CIN. Usinginformation available on the CIN, the ADAC continually tracks both cooperative and uncooperative aircraft. Ifnecessary the ADAC sends commands to cooperative aircraft to avoid Loss of Separation (LOS) scenarios. Theaircraft can be manned or unmanned. This paper describes the implementation of a specific ADAC and CIN to testan automated separation management concept.

II. ADAC ImplementationFigure 1 illustrates a specific centralized ADAC concept used to support the flight trial described in Section V.

The ADAC communication function is provided by a software layer termed the Message Handler (MH), developedfor BR&T by the University of Sheffield2. The messaging layer allows the ADAC to communicate with externalentities such as real or simulated aircraft and radar data feeds. In this study, the communication datalinks to theADAC are real (not simulated communication models) which allows for real-world communication data exchange.While the research and development of a future global Aeronautical Telecommunication Network (ATN) to supportATM is still in progress,3,4 this study uses existing commercial networks (Iridium5,6 and 3G cellular telephony) toprovide the datalink infrastructure necessary to evaluate the automated SM concepts. An assessment of theseexisting datalinks under the context of future aeronautical communication systems can be found in Ref. 7. Suchdatalink technologies could provide a suitable alternative for research projects that would otherwise be reliant on theavailability of a global ATN solution. The MH communicates with the ADAC Separation Manger (SM) via aprescribed interface and in effect, isolates the SM from the task of managing the external communication links.BR&T together with the University of Sheffield and the CSIRO ICT Centre have developed a suite of 6 Degree-of-freedom (6DOF) aircraft models that are compatible with the ADAC communications structure. The ADAC alsoincludes operator visualization tools so that an operator can visualize airspace separation conditions and the actionsof the SM.

TCP/IP connections over the Internet are used to provide the communications links between the ADAC andaircraft. The use of TCP/IP over the internet is not considered a major constraint since most digital commercial

Figure 1. ADAC Architecture for Prototyping Separation Assurance Process in Class G Airspace.

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communications systems (such as Iridium and the 3G cellular network discussed in this paper) provide servicesallowing Internet connections. Specialized non-TCP/IP datalinks can be easily added to the ADAC as required. AnIridium service called Router-based Unrestricted Digital Internetworking Connectivity (RUDICS) allows anyaircraft with a transceiver and antenna to send data through the Iridium satellite system, to a ground based gateway,and establish a bi-directional ADAC connection over the Internet. For communications utilizing the 3G cellularnetwork, a Network Address Translation (NATS) router is used to manage the dynamic IPs assigned to differentaircraft by the service provider. The ADAC MH and SM are capable of handling multiple redundant communicationlinks thus increasing the reliability, coverage and availability of the critical communications function. Thiscommunications architecture implies that the ADAC need not be geographically co-located with the aircraft and canin fact be located anywhere on the globe where there is Internet connectivity. In addition, the architecture cansupport redundant and geographically distributed ADAC systems.

To enable communication with the ADAC all participating aircraft require integration with a flight managementsystem modified with a predictive capability. The Predictive FMS (pFMS) estimates the future aircraft state(position, attitude and time), manages the multiple communications links comprising the CIN, manages commandsreceived from the ADAC, and intelligently manages onboard sensors and sensor information (such as passive sense-and-act systems). In future, the pFMS can support decentralized aircraft separation architectures. However, for thetests discussed in this paper the pFMS does not provide a decentralized separation function and instead provides thesoftware interface between the aircraft management system and the ADAC.

Cooperative aircraft within a region of interest periodically transmit an aircraft state vector and a flight plan, ifavailable. The flight plan can also contain an expected average speed between waypoints. The aircraft state vectorreceived by the ADAC from a cooperative aircraft is termed as Trajectory Array Data Sets (TADS) and comprisethe following data elements: time; and current and predicted latitude; longitude; altitude; ground speed; heading;pitch and roll. The TADS contains both position and speed/attitude information and is sometimes referred to as a 7Dinformation set when compared to a 4D set which would involve only time and position information. The 7Dinformation set is useful for calculating short term predictions of future aircraft positions.

The pFMS transmits a binary data message to the ADAC at a nominal rate of 1 Hz. There are approximately 650bits per state vector, including message overheads. The ADAC may also receive radar track data via a directconnection or over the CIN . The SM uses the TADS and radar data to estimate the current airspace conditions andto determine future possible Loss of Separations (LOS) events. If a future LOS is detected cooperative aircraft areautomatically issued trajectory modifications, in the form of a short-term flight plan, to ensure safe aircraftseparation. The flight plan modification issued from the ADAC is termed Commanded TADS (CTADS). To providea human operator with decision support, the entire aircraft tracking and CTADS generation process is displayed on avisualization display tool. In addition to nominal flight plan, TADS and CTADS messages, the ADAC and aircraftexchange additional application protocol overhead messages including pings, local situational awareness (provide anaircraft with information on local traffic) and acknowledgement messages.

Figure 1 can be viewed as a closed loop process where the aircraft continually issues state information to theADAC and the ADAC monitors and attempts to control the aircraft states if prescribed LOS thresholds are violated.This end-to-end closed loop process is assumed to be continually running for a specified airspace region with anapproximate end-to-end latency of 2-3 seconds per closed loop cycle. The region of interest for our current ADACspec is an approximate 10nmi x 20nmi region containing a maximum of 25 aircraft.

III. Aircraft Separation ManagementThe Separation Manager is a key software component in the ADAC. The ADAC architecture illustrated in Fig. 1

can be used to evaluate a range of SM algorithms. The SM provides several functions including:

• Interfacing with the Message Handler to receive and transmit messages via the CIN.• Track aircraft using the TADS.• Predicting the future trajectories of aircraft based on TADS and other received data (i.e., TADS

received from a radar feed or from sense-and-act systems onboard a cooperative aircraft). • Detection of future LOS events based on the predicted trajectories.• Determination of a set of flight plan modifications to ensure safe aircraft separation.

The algorithm evaluated in this paper is named the Virtual Predictive Radar (VPR). This proprietary algorithmdeveloped by a contributing author, Dr. Estkowski, uses input data from cooperative aircraft and other sources tobuild a synthetic radar-like display, to estimate the times and positions of future LOS events. Currently, the SM

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aligns the final waypoint of any CTADS so that it is coincident with a waypoint on the nominal aircraft flight plan.This ensures that commanded avoidance maneuvers always return cooperative conflicting aircraft back to theirdesired flight plan once separation has been assured.

The VPR algorithm is being used as the primary separation assurance algorithm within the SM for all currentlyplanned flight tests. It is actively under development with additional features being added as “lessons learned” areincorporated. Future plans include testing and comparing alternative separation assurance algorithms. TheUniversity of Sheffield is developing one such algorithm, based on a Genetic Search Algorithm8. The current ADACarchitecture provides the ability to evaluate and compare alternative algorithms with simulated aircraft with orwithout communications hardware in the loop. These results from these comparisons will be published in a futurepaper.

IV. Smart Skies ProjectThe Smart Skies Project (Smart Skies) is a three-year collaborative research and flight test program exploring

future technologies that support the safe and efficient utilization of airspace by both manned and unmanned aircraft.The Project, which commenced in March 2008, brings together specialist researchers from Boeing Research &Technology (BR&T), Boeing Research & Technology Australia (BR&TA), and the Australian Research Centre forAerospace Automation (ARCAA); a joint venture between the Commonwealth Scientific and Industrial ResearchOrganization (CSIRO) ICT Centre, and Queensland University of Technology (QUT), to explore the developmentof three key enabling technologies:

1) An Automated Separation Management System capable of providing separation assurance in complexairspace environments.

2) Sense and Act (SA) systems for manned and unmanned aircraft capable of collision avoidance ofdynamic and static obstacles.

3) A networked-enabled Mobile Aircraft Tracking System (MATS) comprising a cost-effectiveCommercial Off The Shelf (COTS) radar and an Automatic Dependent Surveillance - Broadcast (ADS-B) system.

Central to the Smart Skies Project are a series of integrated flight trials to be conducted near the township ofKingaroy (Queensland, Australia). The objective of these flight trials is to characterize the performance of thedeveloped technologies under realistic and stressing operating conditions. The first set of tests, named Phase 1 FlightTrials (P1FT), was completed in early April of 2009 and some of the results are shown in Section V. Phase 2 FlightTrials (P2FT) were accomplished in July of 2009, too late for any analysis to be in this paper. These initial flighttrials are oriented toward the Automated Separation Management System enabling technology 1. BR&T hasimplemented the specific Smart Skies ADAC (Section II) to satisfy the Automated Separation Management Systemtechnology requirements. To demonstrate the geographical independence of an ADAC, the control center wasphysically located in Seattle, WA during Phase 1 flight trial. The location of the Phase 2 ADAC was in Palmdale,CA.

The primary flight test aircraft used in the P1FT and P2FT include:

1) A Cessna 172R aircraft referred to as the Airborne Systems Laboratory (ASL). The custom-modifiedaircraft is fitted with a GPS-INS truth data system, pFMS, custom flight display (for visualizing flightplans, CTADS and other information received from the ADAC) and a communications managementsystem. The ASL is capable of conventional human piloted control or an optionally piloted mode(enroute lateral autopilot). For P1FT the ASL was flown under a conventionally piloted mode ofoperation, and for P2FT the ASL was flown in an optionally piloted mode of operation.

2) A small autonomous fixed-wing UAS, referred to as the QUAS. The QUAS has a maximum takeoffweight of 20kg, a payload capacity of 4kg and an endurance of approximately one hour (full fuel andpayload). Onboard systems include a pFMS, COTS autopilot, UHF, Iridium and 3G communications,and a vision-based sense and avoid payload.

3) A small autonomous helicopter, referred to as the CUAS. The CUAS has a maximum takeoff weight of13kg and endurance of approximately 45 minutes (full fuel and payload). Onboard systems include: a

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pFMS, custom-designed flight computer and autopilot, UHF communications, and an integrated LIghtDetection And Ranging (LIDAR) and stereo vision-based sense and avoid payload. Iridium and 3Gcommunications systems are located at the CUAS ground control system.

In addition to the real flight test aircraft described above, multiple pseudo aircraft are simulated using the 6 DOFUniversity of Sheffield and CSIRO aircraft simulation systems (located at Sheffield, United Kingdom, and atBrisbane, Australia, respectively). The use of simulated aircraft in combination with real aircraft and realcommunications links provides a safe and efficient testing environment for the evaluation of complex LOSscenarios. Two communication systems are currently being evaluated as part of the CIN: the Iridium RUDICSsystem and the Telstra Next GTM cellular system (3G).

The focus of P1FT (Section V),and P2FT is on the evaluation of the performance of the ADAC with the ASL,CUAS, QUAS and multiple simulated aircraft. Future Smart Skies Flight tests will include enabling technologies 2and 3. In technology 2, a vision-based sense and act system will be integrated onboard the QUAS. Such a systemcould be used for collision avoidance in situations where ADAC SM facilities are not available, or to increaseseparation reliability (provision of another layer of defense). An integrated LIDAR and stereo vision camera systemwill also be implemented on the CUAS for the detection of ground based obstacles. Both the collision avoidance andstatic obstacle sensors can be used as additional target information sensors (nodes) which can be used to supplementthe situational awareness of the ADAC. The characterization and performance testing of these two sensor systemswill be part of Phase Three Flight Trials (scheduled for late 2009).

Enabling technology 3 will integrate mobile Radar facilities in a remote area of interest to provide the SMprocess with radar track data on uncooperative aircraft. The development of all three enabling technologies will becritical in ultimately integrating manned aircraft and UAVs safely to freely fly in Class G airspace.

The remainder of this paperfocuses on enabling technology 1.Key to the development oftechnology 1 is the establishment ofreliable communications betweencooperative aircraft and the ADAC.This communications capability isreferred to as a CommonInformation Network (CIN). TheSmart Skies CIN includes theIridium Satellite System and theTelstra Next Generation (3G)cellular system, referred to as NextGTM. Figure 2 shows how theADAC and CIN were implementedfor P1FT.

The Iridium System has theadvantage of near global coverage.Aircraft flying in Australianairspace, installed with an Iridiumtransceiver and antenna,communicates with Iridiumsatellites and establish a full duplex

2400 bits per second data call with the Iridium RUDICS server. The number of data call channels available farexceeds any Smart Skies test requirements.

The Next G cellular system was designed for ground cellular communications. However the system works wellfor aircraft where coverage is available. Extensive testing over the test site at Burrandowan indicated that Next Gcoverage was sufficient for the flight trials. The Next G data rate can be up to several Mbps, depending on thenumber of users on the system.

Figure 2. Smart Skies Phase 1 Test Architecture Overview.

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V. Smart Skies Phase 1 Flight TrialThe Smart Skies Phase 1 Flight Trial (P1FT), executed on 30 March - 5 April 2009, was the first in a sequence

of flight trials to be run in 2009-2010. These trials intend to demonstrate automated airspace tracking,communications, and control in Class G airspace. As previously mentioned, for P1FT the ADAC was located inSeattle WA. The role of the ADAC in the flight trial was to automatically track and, if necessary, issue safeseparation flight plan modification commands to simulated and real aircraft in Australia over commercial satelliteand terrestrial cellular communication links. For these Phase 1 Flight Trials any aircraft mutual separation distancegreater than 1 km was deemed safe. Figure 3 lists the different test cases for this flight trial.

The primary objective of the ADAC P1FT was to verify simulated airspace control scenarios with actual datacollected from flight tests using the Smart Skies ASL and CUAS. Scenarios resulting in a LOS with simulatedaircraft were pre-planned. Only pair-wise potential collisions were planned for this first trial. All aircraft were flownand simulated at a common altitude. The SM issued CTADS at a common altitude and was restricted to lateral flightplan changes for the aircraft. The pFMS display within the ASL, translated the CTADS flight plan information intowaypoint tracking instructions. The ASL pilot and operator monitored the display and flew the commanded flightplan. In P2FT commanded flight plans were input directly to the ASL autopilot. A total of 21 out of 24 planned testcases were executed during P1FT. Half of the planned tests used Iridium as the primary channel and remaining halfused Next G. Only 7D information content was tested. During the execution of the tests, it became clear that therewas no difference in SM performance regardless of whether Next G or Iridium was the primary communicationchannel.

The first eight test cases, Test 1A Segment 1, (Figure 3, top left) were designed to have LOS conditions betweenthe ASL and a simulated C172R at different relative speeds with a constant angle of approach of 180º (head-on).

Test cases for Test 1A Segment 2 (Figure 3, top right) varied the angle of approach from 135º to 0º in 45º stepsto observe the effect on the resultant separations. The manned ASL flew the oval while the simulated aircraftintersected with the ASL’s nominal flight path at predetermined points at the appropriate angle. Three of the test

Figure 3. Phase 1 Trial Planned Test Cases.

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cases, 2-3, 2-6 and 2-8 were not run because the necessary data for the same geometry had been collected usinganother data channel.

Test cases for Test 1A Segment 3 (Figure 3, bottom left) had the ASL and simulated aircraft constrained in aFigure 8 shaped trajectory, going in opposing directions. This forced several loss of separation conditions while bothaircraft were mid-turn. Potentially, multiple LOS’s could occur on this trajectory, depending on the relative timingof the simulated aircraft and the ASL.

Test cases for Test 1C (Figure 3, bottom right) were composed of four tests with the CUAS flying a zigzagpattern at the bottom of the oval. The measured altitude of the CUAS was less than 400 ft AGL. To force a LOScondition the SM artificially adjusted the altitude to thatof the ASL.

Figures 4 and 5 illustrate two of the 21 test casesexecuted during the trial. In Figure 4 separation isachieved on the oval shaped flight plan. It shows thenominal flight plan between the manned ASL (AID 6)and a simulated C172 (AID 1) running on a computer atthe CSIRO ICT Centre (Brisbane). Both aircraft weretracked and controlled from the ADAC in Seattle. TheASL and CUAS maintained VHF truth datalinks with theSmart Skies Mobile Operations Center MOC and theTrial Test Director coordinated test activities via VHFvoice communications with the manned aircraft, ADACand the CUAS operator as required. For this test case thenominal flight plans were developed so that a LOS wouldoccur on the right-hand side of the oval. The dotted linessuperimposed on the nominal flight plans are themodified flight plans (CTADS) issued automatically bythe ADAC to achieve a safe separation. In this case boththe simulated aircraft and the ASL received CTADS. Inother words, each aircraft shared the cost of theseparation maneuver. For this test case the Distance ofClosest Approach (DCA) was approximately 1.7 km andthe separation was deemed successful. The MOC is alsoillustrated at the bottom of the oval.

Figure 5 shows another oval test where the ASLavoids the CUAS helicopter. The CUAS was constrainedto fly not more than a few hundred meters from itsassociated ground station. In this case the CUAS (AID 5)is considered a semi-cooperative target. This means thatthe ground station transmits TADS from the CUAS to theADAC but the CUAS does not follow a flight plan oraccept flight plan updates. During this test only the ASL(AID 5) received CTADS to avoid a LOS condition. TheDCA in this case was > 2 km.

Figures 4 and 5 also illustrate the output of the ADACvisualization tool named Smart Skies BattlescapeDevelopers Option (SSBDO). This tool allows thevisualization of the aircraft in real-time OpenFlight(TM)aircraft models. SSBDO and the SM are separateprocesses, with the SM transmitting the relevantinformation to the SSBDO over a dedicated TCP/IPconnection.

Planned ASLTrack

CUAS Heli

CTADSWaypoints

ASL

Planned ASLTrack

CUAS Heli

CTADSWaypoints

ASL

Figure 5. P1FT CUAS Case.

Test Cessna AC ID 6

Pseudo AC ID 1

Nominal Flight Plan

Safe Flight Plan UpdatesGenerated in Real Timeby SM

Burrandowan MissionOperation Center

Distance Between AC

Test Cessna AC ID 6

Pseudo AC ID 1

Nominal Flight Plan

Safe Flight Plan UpdatesGenerated in Real Timeby SM

Burrandowan MissionOperation Center

Distance Between AC

Figure 4. P1FT Oval Case.

American Institute of Aeronautics and Astronautics8

Observed data communications between the test aircraft and the ADAC were considered excellent during thistest. Pre-test analysis and verification by the QUT team verified that the latency and call dropout rate over Iridiumwas acceptable for flight tests. The planned data rate from the ASL and CUAS for Iridium were 0.5 Hz and 1Hz forNext G. The data rate from the simulated aircraft was 2.5 Hz. Figure 6 shows the results of a 24 hour ground testusing Iridium where a ping message was sent and received through the Iridium system. Approximate round triplatencies had a mean value of 1.8 seconds. Nine dropped calls occurred during this 24 hour period. A typicaldatalink outage lasted between 10-25 seconds though three longer drop outs were recorded during this period.During the flight trials typical round trip latencies of approximately 2 seconds were observed. This latency includedthe processing times on each side of the interface. During the live tests no dropped calls were observed with theIridium data channel. This result was better than the initially expected performance and may be explained by the factthe data for Fig. 6 resulted from ground experiments, where there is usually a less than ideal sky view for thetransceiver antenna. The aircraft Iridium antennas face upward and have no such obscuration of the sky when theyare flying. Figure 7 shows a pre-test evaluation of Next G latencies. In this case the data is shown as a histogram.The round trip latencies range from 20 ms to just under 700 ms. During P1FT round trip latencies of approximately0.9 seconds were observed (including ADAC and pFMS processing). Next G cellular coverage varies slightly withtime as a function of the network load and the location of the aircraft relative to towers. Next G drop-outs occurredrarely during the tests. The coverage of the Next G network at altitude was better than expected as the system hasbeen optimized (e.g., antenna geometries and distribution of towers) for ground-based users.

A key metric in the evaluation of the CIN formed by Iridium and Next G are the number of dropped messagesobserved during the tests. Typical performance values for Next G and Iridium were on the order of 0.99 or better(less than one out of 100 messages was lost or rejected). This value includes the processing of the messages on eachend of the datalink so the actual loss due solely to communications is actually better than 0.99.

In P1FT the SM used only one communication channel per test, even though the ADAC was receiving both theNext G and Iridium channels. In P2FT and future testing, both channels are utilized so that if one channel fails theother will be available. Active redundancy in communication systems greatly improves the overall reliability of thesystem. The Iridium and Next G communications channels may be considered as independent communicationchannels, indeed the Iridium antenna is mounted on top of the aircraft facing upward whereas the Next G antenna islocated on the bottom facing downward. Adding independent random variables with probabilities of 0.99 (summingthe probabilities and subtracting the product) together gives a combined theoretically reliability of 0.9999 when bothchannels are available. Increasing the number of redundant communication channels has the potential of increasingcommunication reliabilities to approach or surpass that needed for safety standards required for controlled airspace.

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300

350

400Latencies Including Dropouts: Local Clock

Samples

La

ten

cy(s

eco

nd

s)

Duration of Test = 23.8526 HoursNumber of Corrupted Records = 0Number of Recorded Outages = 9.5Mean Latency = 1.828 SecStdDev Latency = 4.1041 Sec Approx

round triplatency of1.8 sec

Figure 6. Example Iridium Latencies

100 150 200 250 300 350 400 450 500 5500

100

200

300

400

500

600

700

Frequency

Late

ncy

(ms)

Figure 7. Next G Latency Histogram

American Institute of Aeronautics and Astronautics9

VI. Metrics for Evaluation of the SMBR&T have developed several metrics to evaluate the performance the SM VPR algorithm version 2.2 in a

realistic environment. The primary focus of this flight test was to establish reliable interfaces and gather an initialdata set to serve as a stepping stone to more complex tests later in the Smart Skies flight trials program. As a resultthe VPR SM entered the test with a few liens. One lien, due to the tight development schedule, was the omission ofan accurate short-term aircraft prediction capability in version 2.2. This meant that when the SM did not have anaccurate estimate of the aircraft future aircraft positions when an aircraft significantly deviated from the flight plan.This will be rectified in future tests with the implementation of an accurate short-term prediction capability based onthe received TADS data.

The metrics presented below are considered to be a work in progress and will evolve over the Smart Skies FlightTest program. Significant amounts of data have been collected facilitating the evaluation of any metrics desired.Results of the metric evaluation for all test cases are shown in Appendix 1.

Our first metric is the Distance Metric, which evaluates the Distance of Closest Approach (DCA) between thetwo aircraft. The algorithm was considered satisfactory if the DCA was greater than 1 km but not larger than 3 km.The purpose of specifying an upper limit on the DCA metric is to ensure that separation solutions do not come atunnecessary cost (in terms of flight plan deviation, hence fuel and time) to airspace users. Figure 8 shows therelevant data analyzed for the test cases A1-5 (on the left hand side of the oval) and A1-6 (on the right hand side).The upper half of the figure shows the plots of the actual test and the pre-test planned trajectories. The bottom halfshows the mutual distances versus time of the same trajectories. In this case and in general the SM worked quitewell. There were a few cases, as noted in Tables A2 and A3, where the acceptable DCA was not achieved. Post testanalysis indicates that the SM lack of an accurate short term prediction caused this problem.

The next metric involves estimating the amount of ‘control’ needed to effect a safe separation. P1FT cases wererestricted to lateral maneuvers so the control needed for separation may be estimated by comparing the roll timehistories of planned versus actual scenarios. This metric can be used to evaluate the relative performances of twoSM algorithms. Since we are only evaluating the results for SM 2.2 we can only establish a baseline that can be usedfor further investigations.

Two metrics were evaluated for estimating control. The first was to estimate the number of turns accomplishedwithin the planned and actual trajectories. A turn was defined to be a point in the trajectory where the slope of theroll time history changed sign from the previous sample and where the concavity of the roll at this point was greater

Actual Trajectories Planned Trajectories

Actual Separations Planned Separations

Actual Trajectories Planned Trajectories

Actual Separations Planned Separations

Figure 8. Example Separation Evaluation

American Institute of Aeronautics and Astronautics10

than a specified threshold. The threshold was defined in order to discriminate between noisy data (sensor and wind)from that of actual turns.

This method worked well for simulated trajectories where turns are few and well defined. Actual trajectories,taking place with a pilot steering in a dynamic atmosphere, results in a significant increase in turns as shown inFigure 9. This led us to fly and record control trajectories. A control trajectory is a nominal flight of the ASLaround the oval or figure of eight flight plans without separation so a comparison could be made with the separatedtrajectories.

A separate metric calculation, referred to as the Roll Metric, was also used to estimate the amount of controlissued. This metric estimates the amount of control by computing the area under the curve of the absolute value ofthe roll curves as shown in Figure 10. This was also compared with the control trajectories. The value is given byEquation 1.

( )[ ]∫=timefinish

timestartttrollabsRollarea

_

_δ (1)

Both the turn and roll calculations where normalized with the control and planned pseudo target trajectoriesusing the simple calculation given by Equation 2.

( )( )

+−

−=plannedactual

plannedactualabsMetric 1 (2)

Turn calculation gives 297 turns

Turn calculation gives 53 turns

Turn calculation gives 297 turns

Turn calculation gives 53 turns

Figure 9. Example Turn Calculations.

American Institute of Aeronautics and Astronautics11

The Appendix lists metric values for the various test runs. The values listed in the Appendix will be used ascomparison with future evaluations of the SM.

Two additional metrics were considered when analyzing the P1FT dataset. Both metrics measured how well theaircraft returned to the original flight plan after a separation maneuver. One metric measured how quickly anaircraft undergoing a separation would return to plan. The time of arrival deltas at a final waypoint on the trajectorywere compared. A similar metric comparing total flight path arc length between planned and actual trajectories wasalso compared. The metrics are being refined and the results are not included in this paper.

There are other factors relating to the SM performance that should be considered. One is the time is takes totrack the aircraft, evaluate potential LOS conditions, and if necessary issue separation commands. Our code was notproperly instrumented to record this value but from the received data we can infer that the execution time wasgenerally < 1second per cycle. Another, harder to quantify, is the robustness of the SM. An example of version 2.2SM robustness occurred in P1FT when the ADAC and ASL clocks drifted apart. The ASL synchronizes with a timeserver prior to take-off and the ADAC synchronizes prior to each test run. In addition to normal drifts the ADACwould occasionally experience clock drifts of approximately 100 sec over a 30 minute flight test. The SM 2.2accounted for this unexpected clock, using the ping messages and associated time stamps to automatically correctfor time differences. Another factor showing the robustness of SM 2.2 was the excellent results presented in theAppendix in spite of no short-term predictor capability. This capability was purposely left out to reduce thecomplexity of the P1FT. Inclusion of the prediction process will be in all subsequent trials and which will enablesuccessful separations of more complex airspace scenarios.

VII. ConclusionThis paper documents an approach for evaluating SM algorithms in uncontrolled Class G airspace. The ADAC

concept successfully remotely tracked and commanded real and simulated aircraft from a remote location using theInternet and existing commercially available wireless networks. This methodology has the advantage of safelytesting separation assurance algorithms with real manned and unmanned aircraft, additional simulated aircraft toaugment reality, and hardware-in-the-loop communication links. The use and comparison of two differingcommunication channels (Iridium and Next G) has shown that tracking and control applications can be successfulwith relatively low bandwidth links. Detailed results are given for the Version 2.2 VPR SM algorithm and aframework has been developed for the evaluation of additional algorithms in the future.

ASL Control Trajectory Roll Metric = 6255 deg-secASL Control Trajectory Roll Metric = 6255 deg-sec

Figure 10. Roll Metric Calculation

American Institute of Aeronautics and Astronautics12

Appendix

A. Phase 1 Test Results

1. Test 1A Segment 1 ResultsTable A1 lists the analysis results for the 8 Test1A Segments 1. The objective of this test was to investigate SM

performance with different ASL closing speeds and for different communication paths (Iridium and Next G). Novariation of SM was observed in these test cases. Note that on complete test run around the oval included twoseparate test cases. In the case of communications, roll, and turn metrics both test cases were combined and notanalyzed separately. The parameters for the ASL control trajectory are also listed.

Date of Test

PlannedASLSpeed(knots) Comms

ApproachAngle Info Test Target

targknots(mean)

targknots(stddev)

ASLknots(mean)

asl knots(std dev) DCA

SMTargctads

SMaslctads

Rollmetric

turnmetric

CommsNext G

CommsIridium

CommsInternet

3/30/2009 75 NextG 180 7D 1A_1-1 sas 94 3 90 8 2.4 1 1 0.94 0.88 0.997 0.9983/30/2009 95 NextG 180 7D 1A_1-2 sas 2.0 1 13/30/2009 115 NextG 180 7D 1A_1-3 sas 95 3 104 12 2.5 1 1 0.89 0.99 0.994 0.9973/30/2009 95 NextG 180 7D 1A 1-4 sas 2.2 1 13/30/2009 75 Iridium 180 7D 1A_1-5 sas 95 3 90 8 2.4 1 1 0.97 0.9 0.995 0.9973/30/2009 95 Iridium 180 7D 1A_1-6 sas 2.3 1 13/30/2009 115 Iridium 180 7D 1A-1-7 sas 95 4 108 11 2.6 1 1 0.96 0.81 0.992 0.997

Control 96 7

Test Parameters For Phase1A Segment 1 Testing Phase 1A Segment 1 Test ResultsSceanrio Speeds SM Perfromance Results Communication Results

Table A1. Test 1A Segment 1 Results.

American Institute of Aeronautics and Astronautics13

2. Test 1A Segment 2 ResultsTable A2 lists the analysis results for five Test1A Segment 2 cases. The objective of this test was to investigate

SM performance with different ASL angle of approaches and for different communication paths (Iridium and NextG). Angle of approaches cases 2-7 and 2-4 failed to meet the DCA threshold (Angle of approach 45 and 0 deg.).Three cases, 2-3, 2-5 and 2-8 were deemed not needed because data for the same geometry had already beencollected over another data channel.

3. Test 1A Segment 3 ResultsTable A3 lists the analysis results for six Test1A Segment 3 cases. The objective of this test was to investigate

SM performance with ASL curvilinear trajectories and for different communication paths (Iridium and Next G). Acombination of pseudo target simulation problem and the lack of an accurate short term prediction implementationlet to the DCA issues. Note that one complete run around the figure 8 trajectory resulted in three distinct separations.

4.

PlannedASLSpeed(knots) Comms

ApproachAngle Info Test Target

targknots(mean)

targknots(stddev)

ASLknots(mean)

asl knots(std dev) DCA

SMTargctads

SMaslctads

Rollmetric

turnmetric

CommsNext G

CommsIridium

CommsInternet

3/31/2009 95 NextG NA 7D 1A_3-1 sas 93 10 103 7 2.3 1 1 0.82 0.92 0.958 0.883/31/2009 95 NextG NA 7D 1A_3-2 sas 2.7 1 03/31/2009 95 1A_3-2a sas 0.93 14 174/6/2009 95 Iridium NA 7D 1A_3-5 sas 97 12 104 8 2 1 1 0.67 0.85 0.989 0.94/6/2009 95 Iriidum NA 7D 1A_3-6 sas 6.8 1 144/6/2009 95 1A_3-6a 0.18 11 33

Control 100 8

Test Parameters For Phase1A Segment 3 Testing Phase 1A Segment 3 Test ResultsSceanrio Speeds SM Perfromance Results Communication Results

Table A3. Test 1A Segment 3 Results.

Table A2. Test 1A Segment 2 Results.

PlannedASLSpeed(knots) Comms

ApproachAngle Info Test Target

targknots(mean)

targknots(stddev)

ASLknots(mean)

asl knots(std dev) DCA

SMTargctads

SMaslctads

Rollmetric

turnmetric

CommsNext G

CommsIridium

CommsInternet

4/6/2009 95 NextG 135 7D 1A_2-1 sas 99 6 95 5 2.2 1 1 0.83 0.99 0.989 0.9964/6/2009 95 NextG 90 7D 1A_2-2 6dof 95 0 95 4 2.1 1 1 0.78 0.92 0.989

3/31/2009 95 Iridium 45 7D 1A_2-7 sas 79 4 77 5 0.3 3 1 0.93 0.87 0.998 0.9113/31/2009 95 NextG 0 7D 1A_2-4 sas 81 9 92 18 0.6 32 16 0.36 0.95 0.958 0.9173/31/2009 95 Iridium 135 7D 1A_2-5 sas 82 6 77 8 1.7 35 2 0.83 0.85 0.996 0.996

Test Parameters For Phase1A Segment 2 Testing Phase 1A Segment 2 Test ResultsSceanrio Speeds SM Perfromance Results Communication Results

Test Parameters For Phase1A Segment 3 Testing Phase 1A Segment 3 Test Results

PlannedASLSpeed(knots) Comms

ApproachAngle Info Test Target

targknots(mean)

targknots(stddev)

ASLknots(mean)

asl knots(std dev) DCA

SMTargctads

SMaslctads

Rollmetric

turnmetric

CommsNext G

CommsIridium

CommsInternet

4/6/2009 95 NextG 135 7D 1A_2-1 sas 99 6 95 5 2.2 1 1 0.83 0.99 0.989 0.9964/6/2009 95 NextG 90 7D 1A_2-2 6dof 95 0 95 4 2.1 1 1 0.78 0.92 0.989

3/31/2009 95 Iridium 45 7D 1A_2-7 sas 79 4 77 5 0.3 3 1 0.93 0.87 0.998 0.9113/31/2009 95 NextG 0 7D 1A_2-4 sas 81 9 92 18 0.6 32 16 0.36 0.95 0.958 0.9173/31/2009 95 Iridium 135 7D 1A_2-5 sas 82 6 77 8 1.7 35 2 0.83 0.85 0.996 0.996

Test Parameters For Phase1A Segment 2 Testing Phase 1A Segment 2 Test ResultsSceanrio Speeds SM Perfromance Results Communication Results

Test Parameters For Phase1A Segment 3 Testing Phase 1A Segment 3 Test Results

American Institute of Aeronautics and Astronautics14

Test 1C ResultsTable A4 lists the analysis results for four Test1C cases. The objective of this test was to investigate SM

performance with between the ASL and a semi-cooperative CUAS over different communication paths (Iridium andNext G). The SM performance was as expected.

AcknowledgmentsThe authors would like to acknowledge Mr. Ted Whitley who is supervising BR&T’s effort in the Smart Skies

project. Ted also developed the concepts of the ADAC and has long been a proponent of automated air trafficcontrol. The authors also acknowledge the advice and support of Professor David Allerton on the University ofSheffield. In addition acknowledgement is give to members of the Boeing Iridium Support and Battlescapedevelopment teams for their detailed subject knowledge and support. Finally we are happy to acknowledge allmembers of the Australian branch of Smart Skies Team. This team, headed by Professor Rod Walker and directedby Mr. Reece Clothier, accomplished amazing engineering design and development tasks in a very short period oftime in order to conduct the tests Phase 1 Trial documented in this paper.

References1ICAO, “Automatic Dependent Surveillance. Circular 226-AN/135”, International Civil Aviation Organization, 1983.2Spence, G.T., Allerton, D.J., “Simulation of an Automated Separation Management Communication Architecture for

Uncontrolled Airspace, AIAA Modeling and Simulation Technologies Conference, August 2009.3Kerczewski, R.J. and Dyer, G., “Eurocontrol/FAA Future Communications Study - Phase II Technology Assessments”,

IEEE Aerospace Conference, 3-10 March 2007, pp. 1-8.4Leconte, K., Riera, N., Schrekenbach, F. and Platt, P., “Data Link Technology Characterization for NEWSKY Aeronautical

Communication Network”, Advanced Satellite Mobile Systems, 26th-28th Aug. 2008, pp. 49-54.5Peterson K.M. “The Iridium Low Earth Orbit Communications System”, Sarnoff Symposium, IEEE Princeton/Central

Jersey, 1994, pp. 13-19.6Fossa C.E, Raines R.A, Gunsch G.H, Temple M.A., “An overview of the IRIDIUM (R) low Earth orbit (LEO) satellite

system”, IEEE National Aerospace and Electronics Conference, 1998, pp. 152-159.7ITT Industries, “Technology Assessment for the Future Aeronautical Communications System”, NASA/CR-2005-213587,

May 2005.8Spence, G.T., Allerton, D.J., “A Genetic Approach to Automated Aircraft Separation”, CEAS European Air & Space

Conference 2009, October 2009. Accepted for publication.

PlannedASLSpeed(knots) Comms

ApproachAngle Info Test Target

CUASknots(mean)

CUASknots(stddev)

ASLknots(mean)

asl knots(std dev) DCA

SMTargctads

SMaslctads

Rollmetric

turnmetric

CommsNext G

CommsIridium

CommsInternet

3/31/2009 95 NextG NA 7D 1C1 cuas 2.6 4.7 92 18 2.9 NA 1 0.9983/31/2009 95 NextG NA 7D 1C3 cuas 4.4 5.9 86 12 2.8 NA 1 0.9913/31/2009 95 Iridium NA 7D 1C5 cuas 3.6 5.5 77 5 2.8 NA 1 0.9983/31/2009 95 Iriidum NA 7D 1C7 cuas 5.6 4.4 77 8 2.7 NA 1 0.987

Test Parameters For Phase1C CUAS Testing Phase 1C CUAS Test ResultsSceanrio Speeds SM Perfromance Results Communication Results

Table A4. Test 1C Results.