Assessing and Estimating Risk of Operating Unmanned Aerial ...

Assessing and Estimating Risk of Operating

Unmanned Aerial Systems in Populated Areas

Christopher W. Lum∗, Kristoffer Gauksheim†, Chris Deseure‡, Juris Vagners§, and Tad McGeer¶

Autonomous Flight Systems Laboratory

University of Washington, Seattle, WA, 98195, USA

In order to operate in the national airspace, a system must have documentation andanalysis to show that it can operate at a satisfactory level of safety. For traditional mannedsystems, this is equivalent to operating a reliable system. However with Unmanned AerialSystems (UAS), a relatively unreliable system can safely be operated provided that therisk to bystanders on the ground is sufficiently low. This paper presents a set of designtools and methodologies which can be used to assess the risk associated with operating anUAS in a potentially populated area. The intended use of the tool is discussed and a riskassessment is provided for an existing UAS.

Nomenclature

Ab Average building area (km2)ADS-B Automatic Dependent Surveillance-BroadcastALHp Lethal area for pedestrians in a horizontal crash (due to system failure) (km2)d Horizontal distance traveled during a failure (m)Ffat,p Fatalities due to system failures (fatalities/hour)h Height of aircraft at time of failure (m)Hp Average pedestrian height (km)Lua Length of UA (km)MOA Military Operating AreaNAS National AirspaceSAA Sense and AvoidTBO Trajectory Based OperationsUA/UAS Unmanned Aircraft/Unmanned Aerial Systemwua Wingspan of UA (km)α, αH Number of mission collisions predicted by model and historically recorded, respectivelyγ Best glide angle of UA (radians)λ UAS midair failure rate for a single AC (failures/hour)σp Pedestrian densities (items/km2)θ Average glide angle during a failure (rad)

I. Introduction

In the future, Unmanned Aerial Systems (UAS) will interact with general aviation and commercial flightsin many different ways. They will bring with them new technologies and new possibilities in terms of missions

∗Research Scientist, Dept. of Aeronautics and Astronautics, [email protected]†Research Assistant, Dept. of Aeronautics and Astronautics, [email protected]‡Research Assistant, Dept. of Aeronautics and Astronautics, [email protected]§Professor Emeritus, Dept. of Aeronautics and Astronautics, [email protected]¶Affiliate Assistant Professor, Dept. of Aeronautics and Astronautics. Founder, Aerovel Corporation, [email protected]

1 of 13

American Institute of Aeronautics and Astronautics

that could be achieved. UAS will have to respect restrictions and constraints imposed by the Federal AviationAdministration (FAA) in order to achieve an acceptable level of safety and security. Usage of UAS by militaryorganizations around the world has increased dramatically in the last several years. However, growth of UASin the civilian and commercial market has been markedly slower. This is especially true in the U.S. whereconcerns over safe integration and operation of UAS in the national airspace system (NAS) have restrictedthe accessibility of this market. One of the main restrictions is the ambiguous and non-standardized setof design tools and methodologies which are currently employed by various UAS manufacturers in order toassess risk of a mission. This paper documents the development of a simplified model to assess and predictrisk associated with a given UAS operation. This tool is intended to be useful for determining UAS scenariosthat can operate at an acceptable level of safety from the perspective of risk to human safety.

Several efforts have been made in the past to assess the risk of a UAS mission. One of the first effortsinvolved modeling mid-air collisions using random collision theory and comparing results to historical data byAnno.1 Similar work was performed by McGeer2 with extensions involving regulatory policy and economicsof these systems.3

More recently, focus has shifted towards integrating UAS into the NAS.4 A similar risk-based approachto analyzing the safety of UAS operations was taken by Burke5 at North Carolina State University inthe development of the System Level Airworthiness Tool (SLAT). The author also chose to focus on theexpected number of fatalities per flight hour as the primary safety metric. A similar approach was taken byWaggoner6 and Lum7 when developing a design tool to allow the risk associated with a given UAS missionto be calculated.

The current work focuses on building from many of these previously stated ideas in order to develop amethodology which can be used by UAS manufacturers when evaluating the risk of testing their systemsin potentially populated areas. The example used in this paper focuses on performing a risk assessmentrequired to obtain an Experimental Type Certificate for the Flexrotor aircraft8 to perform flight tests atHusum, WA. The main metric for safety is potential harm to bystanders on the ground. Previous results7

have shown that the vast majority of failures of UAS are due to general system failures rather than mid-aircollisions. Therefore, the primary concern is that when a UA fails, it will strike someone on the ground andcause harm.

Additional references and prior work are presented in the context of the federal UAS policy in Section II.This section serves as motivation by looking at current US policy relevant to UAS operations. Section III thendescribes some of the framework used to develop the overall risk model. Results and example calculations ofthis scenario are presented as well. Finally, Section IV presents conclusions and future directions of research.

II. Current UAS Policy

Without a thorough understanding of the risks involved, regulations on the flight of UAS in US airspacehave thus far been highly prohibitive. Policy was set forth in a September, 2005 Federal Aviation Admin-istration (FAA) memorandum,9 clarified in a 2006 notice,10 and replaced in March 2008 by the InterimOperational Approval Guidance.11 Currently, the only avenue to receive approval of civil (i.e. commercial,academia) UAS operation is through a special experimental airworthiness certificate. The special certificateis subject to operational limitations (e.g. line of sight operation, daylight hours, etc.) and is only issued “forthe purposes of research and development, crew training, or market survey.” The procedure and guidelinesfor issuing a special experimental certificate are detailed by the FAA.12

A second avenue, a certificate of authorization (COA), was closed to civil applications in 2005 by FAAmemorandum AFS-4009 but is still used for public (i.e. government/military) requests after the vehicle hasbeen deemed airworthy by the FAA or DoD. A category that a minority of UAS may fall under is modelaircraft13 (strictly non-business related). Other documents of interests include NATO’s UAV Systems Air-worthiness Requirements14 and the European Aviation Safety Agency’s (EASA) statement on AirworthinessCertification of Unmanned Aircraft Systems.15

UAS policy is currently being reviewed to develop a long-term approach to a fluid integration of UASinto the NAS. Several components of the NextGen Air Transportation System (ATS) should help facilitatethis process in the coming years.16 NextGen refers to the next generation of the NAS being incrementallyimplemented over the course of several years, with current mid-term goals set through 2018. Two keyNextGen technologies that have the greatest potential to impact UAS integration are Automatic DependentSurveillance-Broadcast (ADS-B) and 4D Trajectory Based Operations (TBO).17,4 The FAA’s perspective

2 of 13


on UAS has been supportive but cautious as indicated by the following excerpt,9

“The FAA supports UA flight activities that can demonstrate that the proposed operations canbe conducted at an acceptable level of safety. AFS intends to approve COA applications... [if ]a collision with another aircraft...is extremely improbable....[and] injury to persons or propertyalong the flight path is extremely improbable. Acceptable system safety studies must include ahazard analysis, risk assessment, and other appropriate documentation that support the ‘extremelyimprobable’ determination.”

A. Motivation

It is generally perceived that there are a number of obstacles to the full integration of UAS into the NAS. Themost pressing technological challenges are “sense and avoid” (SAA) capability and command and control(C2) link liabilities.4 Sense and avoid refers to the capability of an autonomous vehicle to detect objects,both stationary and mobile, that do not broadcast their position, which are in the vehicle’s path (or otherwiseon a collision course) and, if necessary, alter the vehicle’s course to avoid a collision. Since the pilot of aUAS is not able to provide the “see and avoid” ability of an onboard pilot, the development of reliable SAAtechnology is believed to be essential for UAS to gain full airspace access. Significant work has been doneboth in R&D of SAA technologies and in establishing qualifications for an acceptable SAA system.18

Although most UAa will have low-level autonomy, a reliable communication link between the UA and thepilot is necessary for high-level control (navigation, tasking, air traffic control, etc.). In addition to improvingthe C2 link reliability, protocols must be developed to ensure safe and predictable behavior in the case of alost-link. There is also much work to be done on the policy front. Guidelines are needed on airworthiness,crew training, operational protocols and how UAS will fit into the current and NextGen airspace structures.

Thoroughly addressing all of these issues, so that UAS may be routinely and safely incorporated through-out the NAS, will take years. In the mean time, standards and tools need to be developed that will, “enablethe widest range of activity that can be safely conducted within the shortest rulemaking timeframe” (ASTMF38 Committee). Until new technologies are developed and a new system is in place, UAS operation ap-provals will continue to require mission specific risk assessments.

The purpose of the risk assessment tool presented in this paper is two-fold. First, it seeks to provideUAS operators and airspace regulators with a simplified and trustworthy method of evaluating the safety ofproposed UAS operations. Tools are needed that provide UAS operators with “documentation that supportthe ‘extremely improbable’ determination,” since it is an essential part of the current approval process. Theavailability of a tool to assess the risk of particular proposed UAS operation should make the process ofobtaining approval more efficient and manageable.

The second objective is that the results of risk assessments performed using this tool would supply usefulinformation to the aerospace community as future standards and guidelines are being developed. Successfulregulation will prohibit unsafe operations while clearing the way for operations that do not pose a threat topublic safety. Tools such as this risk assessment procedure will help determine what type of operations posesignificant risk and which do not so that the policies being developed can reflect the risk associated withvarious UAS applications in order to maintain a high level of safety.

III. Design Tools and Methodology for Assessing Risk

The example aircraft used in this analysis is the Flexrotor manufactured by the Aerovel Corporationshown below in Figure 1.

The goal of this work is to assess the relative level of risk associated with operating this aircraft in theHusum, WA area. This same methodology can be applied to other types of UAS in potentially populatedareas.

A. Experimental Type Certificate for Flexrotor

The first step to providing evidence to support the previously mentioned ‘extremely improbable’ determina-tion is to understand and model how system failures ultimately correlate to human injuries and fatalities.

aUA is used to refer only to the aircraft, whereas UAS refers to the whole system inclusive of all ground-based equipmentand any communication links.

3 of 13


Figure 1. Flexrotor is a unique aircraft developed by Aerovel which is able to operate in both traditionalwing-borne flight and hover in thrust-borne flight.

4 of 13


Aerovel is in the process of performing the necessary analysis to obtain an experimental type certificate forflying aircraft in the national airspace. Being able to test the aircraft near the manufacturing facility hasthe potential to save on travel expenses and lost opportunity while waiting for access to restricted airspacesuch as the Boardman, OR test range. This analysis could serve as a model for acceptance of risk analysisby the FAA.

Risk analysis for Flexrotor involves introducing, in simulation, a catastrophic control failure at randomduring flight over the Elsner airfield in Husum, WA. The failure is then simulated to the point of impact.Thousands of such runs are easily done, and allows an estimate of the probability distributions for impactaround the test site. Meanwhile, census data and satellite imagery is used to estimate the local bystanderdistribution. The estimated failure rate of the system is taken from experimental results and hardware-in-the-loop simulation. The upper bound on risk of harm to nonparticipants on the ground can then becalculated as shown in Figure 2.

Figure 2. General outline of risk assessment workflow for Flexrotor.

B. Failure Simulation

A major component of the risk assessment involves analyzing a large amount of failures of the aircraft andsimulating impact zones and areas from these failures. A representative flight of the aircraft is first simulatedand then state information is logged for this flight. The representative flight timeline is described in Table 1.

5 of 13


Table 1. Timeline of representative Flexrotor flight at Husum, WA flight area.

Time (sec) Action

0 Thrust-borne launch

5 5 m/s spanwise slew command to North

90 5 m/s spanwise slew command to South

165 Transition to wing-borne flight begins

177 Transition completes

177 Commanded to 678m altitude

178 Commanded to track 1200m x 300m box

250 Established on north-bound box leg

394 Commanded to 26m/s




717 Commanded to 21.3m/s

735 Go direct to launch/retrieval point

789 Transition to thrust-borne flight begins

797 Transition complete

800 Commanded to 182m (4m AGL)

1135 Established at altitude

1140 Commanded 0.2m/s descent

1166 Touchdown

A failure is then injected to the flight at random times. The failure times are uniformly sampled fromthe representative flight. In order to simulate a flight computer failure, the control surfaces are frozen attheir current state and the engine is shut down. A sampling of approximately 15,000 initial failure points areshown in Figure 3(a). From this point, a failure is simulated by freezing the control surface and simulatingthe dynamics of the powerless aircraft forward in time using a high fidelity, proprietary numerical simulatoruntil the aircraft reaches a preset altitude of 178m (altitude of terrain near Husum, WA). Results of theseforward simulations are shown in Figure 3(b).

As can be seen, the vast majority of crashes occur very close to the initial failure location. This impliesthat once the aircraft experiences a failure, the system rapidly descends and crashes.

Because the failures are introduced at various altitudes, one useful metric would be the average glide anglebetween the initial failure point and the crash location. Geometry of the situation is shown in Figure 4.

From the geometry, we see that the average glide angle is simply given by

θ = tan−1(h/d) (1)

A histogram of the average glide angle is shown in Figure 5(a). As can be seen from this histogram, themajority of glide angles are near 90 degrees, implying that most of the crashes occur near the location of theinitial failure. The benefit of analyzing the glide angle is that it allows the radial distance traveled beforecrashing to be normalized by the altitude. For example, if a mission has the aircraft operating at an averagealtitude of h, for a given glide angle, the radial distance between failure and crash can be computed usingEq. 1 to obtain

d = h · tan(θ) (2)

Eq. 2 can then be applied to the distribution shown in Figure 5(a) to obtain the distribution of horizontaldistance between failure and crash for a nominal operation altitude of 500m. This histogram is shown inFigure 5(b).

6 of 13


−2.1203 −2.1203 −2.1202 −2.12010.7993

0.7993

0.7994

0.7994

0.7995

0.7995

Longitude (radians)

Latit

ude

(rad

ians

)

Initial State Data

(a) Initial location of approximately 15,000 failures sampleduniformly from the representative flight. Representative flightis contained within an 800m wide (east to west) by 1500m tall(south to north) test area.

−2.1204 −2.1204 −2.1204 −2.1203 −2.1203 −2.1202 −2.1201 −2.1201 −2.1201

0.7992

0.7993

0.7993

0.7994

0.7994

0.7995

0.7995

Longitude (radians)

Latit

ude

(rad

ians

)

Final State Data

Original Test Area

(b) Final state data showing location of aircraft crashes.

Figure 3. Impact distribution of aircraft with failures injected to representative flight.

From this figure, we see that the vast majority of crashes (99.97%) occur within 600m of the initialfailure location. We can attempt to analytically fit a polynomial to the histogram. To avoid a badly scaledpolynomial, the tails of the histogram are not fitted, instead, a 6th order polynomial to 99% of the instanceswhich are in the range of d ∈ [0, dmax] where dmax = 350m. Physically, this means that 99% of all crasheshappen within 350m of the failure point. This polynomial fit is determined to have the form of

f(d) =

6∑k=0

akdk (3)

The approximate values of the polynomial constants are shown in Table 3.

Table 2. Approximate constants for polynomial fit of histogram in Figure 5(b).

Parameter Value Parameter Value Parameter Value

a0 588.3 a1 67.0 a2 -1.9

a3 0.02 a4 -0.0001 a5 0

a6 -3.77×10−5 - - - -

Note that f(d) is not a probability density function (PDF). However, it can be converted to a PDF bynormalizing it so that the area under the curve is one. Therefore, for a given failure, the PDF describing thehorizontal distance traveled before impact likelihood in the range of d ∈ [0, dmax] is given by

p̃(d) =1

γf(d) (4)

where γ =∫ dmax

0f(τ)dτ .

Therefore, the total PDF for an entire range of distances is given as

p(d) =

{p̃(d) d ∈ [0, dmax]

0 otherwise(5)

Eq. 5 can then be used to create a radial probability density function to visualize crash potential over a 2dimensional space as shown in Figure 6(a). This crash density representation can be overlaid with a satelliteimage of the test area to determine actual risk to human and structures as shown in Figure 6(b).

7 of 13


Figure 4. Geometry showing average glide angle of aircraft between failure and crash site.

8 of 13


−20 0 20 40 60 80 1000

500

1000

1500

2000

2500

3000

Average Glide Angle θ (deg)

Inst

ance

s

Histogram of Average Glide Angle Between Locked Controls and Crash

(a) Histogram of average glide angle.

−200 0 200 400 600 800 1000 1200 1400 16000

500

1000

1500

2000

2500

3000

Horizontal Distance From Failure Point to Crash Site (meters)

Inst

ance

s

Histogram of Horizontal Distance Using Average Altitude = 500 meters

Histogram

6th order polynomail fit of 99% of instances

(b) Histogram of horizontal distance to impact.

Figure 5. Histograms of relevant data obtained during Monte Carlo simulation of aircraft failures.

(a) Radial crash probability density function vs. radial dis-tance from failure location.

(b) Radial crash probability overlaid with satellite image oftest area near Husum, WA

Figure 6. Radial crash probability function and surrounding structures.

Figure 6(b) shows that 99% of all failures will fall within the circular regions. More specifically, 99%of all impacts will fall within the circular region for a failure at the center of the circle. These regions aremostly unpopulated and therefore, there is a negligible risk to humans or structures. The one exception isthe structure in the northwest region of the test area which actually falls within a relatively high probabilitystrike area.

The population and structural densities are extracted from census data and then analyzed. These struc-tural and population densities can be combined with the crash probability shown previously. The populationdensity of the surrounding ares is shown in Figure 7.

As can be seen, the only area of any calculable risk is in the southwest area of the airfield. This is theonly section where a non-trivial population density intersects the finite crash probability distribution. Notethat at this point, the crash probability is sufficiently low so the product of crash probability and populationdensity is acceptably low. It is also worth noting that moving the flight test area 200m to the northwestwould yield no intersections with the downtown Husum area population. Also note that by operating atlower altitudes, the radius of significant crash probability could also be lowered so that the crash areas do

9 of 13


Figure 7. Population density near the Husum, WA flight test area combined with crash probability distribution.Colored squares denote areas of higher than nominal population density. Crash probability is shown as thecircular areas as previously discussed in Figure 6(b).

not intersect areas of significant population.The two areas of intersection are designed as 1 (area of relatively high population to the southwest) and

2 (area of low population elsewhere). Give a UAS failure, the percentage of crashes which occur in eitherarea are given by

α1 =30o

360o

∫ dmax

220

p(τ)dτ = 0.0135 (6)

α2 =330o

360o

∫ dmax

220

p(τ)dτ +

∫ 220

0

p(τ)dτ = 0.9864 (7)

Based on the probability density function, we see that approximately 1% of UAS failures will occurs inarea 1 (highly populated) whereas the vast majority will occur in area 2 (low population density).

If a failure occurs, the UAS will glide and potentially strike pedestrians on the ground. The lethal areaswhere pedestrians may be struck is illustrated in Figure 8.

The potentially lethal area is given as

ALHp = (wua + 2Rp)(Lua +Hp

tan γ+ 2Rp) (8)

Some other relevant parameters used to estimate the human collisions/hr are shown in Table 3.With these parameters, the expected number of pedestrian collisions per flight hour is given by7

Ffat,p = λALHp(σp,1α1 + σp,2α2) (9)

Using the values for this scenario, a collision rate of Ffat,p = 1.27× 10−5 pedestrian collisions per flighthour is calculated. This translates into roughly one collision every 80,000 hours of operation. Therefore, thisoperation in this area can be considered extremely safe (even with a somewhat conservative estimate of 0.1failures per flight hour).

10 of 13


Figure 8. Geometry showing affected distance covered by UA during a horizontal, gliding crash (red distance).The total affected areas is this distance multiplied by the wingspan of the UA plus 2Rp.

Table 3. Parameters used to estimate human collison/hr for this scenario.

Parameter Value Comment

λ 0.1 failures/hr UAS midair failure rate for a single UA

γ 74.4 degrees Glide angle upon failure. Computed from average glide angle of Figure 5(a)

wua 3m Wingspan of UAS

Lua 1.6m Length of UAS

Rp 0.25 Radius of person

Hp 1.8m Height of person

σp,1 300 people/km2 Population density in area 1

σp,2 10 people/km2 Population density in area 2

IV. Conclusions and Further Research

The methodologies outlined in this paper can be used to assess the risk of a UAS operating in a populatedarea. It allows the user to calculate the estimated number of bystander collisions per flight hour based onreadily available data such as satellite imagery and census information. UAS failure rates can be determinedfrom manufacturer specifications and experimentally obtained data such as hardware-in-the-loop simulation.

The tool is currently being used to develop safety cases which will be both illustrative and directlyapplicable to current issues. This analysis will be used for Aerovel’s application to the FAA to obtain anexperimental type certification for operations in the Husum, WA area. This work is also in the process ofbeing applied to a case study to allow UAS to operate in the Juniper military operation area (MOA).

Current research is directed towards combining this methodology with existing tools7 to assess the riskat different phases of the flight such as prolonged cruise. In addition, factors such as birdstrike and midaircollisions are being added to the model. Various improvements to the current algorithm are also beinginvestigated. These include concepts such as fitting a simpler function to the experimental distributions. Forexample, fitting a 2D Gaussian to the crash PDF will serve to simply the calculations and allow for analyticalcalculation of some parameters. Although this may not fit the actual distribution as accurately, it has beenobserved that a higher fidelity model does not increase accuracy beyond already existing uncertainty bounds.

11 of 13


Another area of improvement is the convolution of the crash PDF with the population density. An improvedmethod would involve creating software to automatically calculate the population density of each pixel of thesatellite image similar to that shown in Figure 9. This information can then be combined with the spatiallyoriented crash PDF in order to calculate a more accurate estimate of bystander collisions.

Figure 9. Satellite image of Husum area overlaid with detailed population density areas. Purple areas representroads, yellow areas represent high population density areas, green areas show medium population density areas,and uncolored regions are considered unpopulated.

The probability density function in terms of both glide angle and crash distance are likely highly de-pendent on mode of operation. For example, thrust-borne maneuvers are likely to be highly unstable andonce the in-flight computer fails, the aircraft crashes almost immediately. Conversely, in a stable wing-borneflight, the aircraft has the potential to glide for a considerable distance before crashing. Therefore, individualPDFs based on operating mode may improve accuracy of estimation by a significant margin.

Incorporating these elements will serve to ameliorate concerns about manned and unmanned aircraftcoexisting in shared airspace.

V. Acknowledgements

The work reported here was sponsored in part by AFOSR SBIR FA8501-10-P-9005.

References

1Anno, J., “Estimate of Human Control Over Mid-Air Collisions,” Journal of Aircraft , Vol. 19, No. 1, 1982, pp. 86–88.2McGeer, T., “Aerosonde Hazard Estimation,” Aerovel Corporation.3McGeer, T., “Safety, Economy, Reliability, and regulatory Policy for Unmanned Aircraft,” .4A. Lacher, A. Zeitlin, D. Maroney, K. Markin, D. Ludwig, and J. Boyd, “Airspace Integration Alternatives for Unmanned

12 of 13


Aircraft,” CAASD, The MITRE Corporation, Feb. 2010.5Burke, D., System Level Airworthiness Tool: A Comprehensive Approach to Small Unmanned Aircraft System Airwor-

thiness, Ph.D. thesis, North Carolina State University, 2010.6Waggoner, B., Developing a Risk Assessment Tool for Unmanned Aircraft Systems Operations, Master’s thesis, University

of Washington, Seattle, WA, 2010.7Lum, C. W. and Waggoner, B., “A Risk Based Pradigm and Model for Unamnned Aerial Vehicles in the National

Airspace,” To appear in the proceedings of the 2011 Infotech@Aerospace Conference, St. Louis, MO, March 2011.8“Aerovel Begins Flight Test of Flexrotor Long-Endurance Robotic Aircraft with VTOL,” Aerovel Press Release,

http://www.aerovelco.com/papers/FlexrotorFirstFlightAnnouncement.pdf.9McGraw, J., “AFS-400 UAS Policy 05-01, Unmanned Aircraft Systems Operations in the U.S. National Airspace System

- Interim Operational Approval Guidance,” Federal Aviation Administration.10Sabatini, N., “Unmanned Aircraft Operations in the National Airspace System,” Federal Aviation Administration.11Davis, K., “Interim Operational Approval Guidance 08-01, Unmanned Aircraft Systems Operations in the U.S. National

Airspace System,” Federal Aviation Administration.12Paskiewicz, F., “Order 8130.34: Airworthiness Certification of Unmanned Aircraft Systems,” Federal Aviation Adminis-

tration.13Vuren, R. V., “Advisory Circular 91-57: Model Aircraft Operating Standards,” Federal Aviation Administration.14“STANAG 4671 Unmanned Aerial Vehicle Systems Airworthiness Requirements (USAR),” NATO Document, Sept. 2009.15“Policy Statement Airworthiness Certification of Unmanned Aircraft Systems (UAS),” European Aviation Safety Agency

(EASA), Aug. 2009.16“FAA’s NextGen Implementation Plan,” Federal Aviation Administration.17F. Martel, R.R. Schultz, W.H. Semke, Z. Wang, and M. Czarnomski, “Unmanned Aircraft Systems Sense and Avoid

Avionics Utilizing ADS-B Transceiver,” April 2009.18“SAA Workshop Final Report: Sense and Avoid for Unmanned Aircraft Systems,” Federal Aviation Administration.

13 of 13


Assessing and Estimating Risk of Operating Unmanned Aerial ...

Documents

Transcript of Assessing and Estimating Risk of Operating Unmanned Aerial ...