Thirteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2019) 

Sense and Avoid Characterization of the Independent 

Configurable Architecture for Reliable Operations of 

Unmanned Systems 

Maria Consiglio 

NASA Langley Research Center 

Hampton, VA, USA 

Brendan Duffy 

National Institute of Aerospace 

Hampton, VA, USA 

Abstract—Independent Configurable Architecture for 

Reliable Operations of Unmanned Systems (ICAROUS) is a 

distributed software architecture developed by NASA Langley 

Research Center to enable safe autonomous UAS operations. 

ICAROUS consists of a collection formally verified core 

algorithms for path planning, traffic avoidance, geofence 

handling, and decision making that interface with an autopilot 

system through a publisher-subscriber middleware. The 

ICAROUS Sense and Avoid Characterization (ISAAC) test was 

designed to evaluate the performance of the onboard Sense and 

Avoid (SAA) capability to detect potential conflicts with other 

aircraft and autonomously maneuver to avoid collisions, while 

remaining within the airspace boundaries of the mission. The 

ISAAC tests evaluated the impact of separation distances and 

alerting times on SAA performance. A preliminary analysis of the 

effects of each parameter on key measures of performance is 

conducted, informing the choice of appropriate parameter values 

for different small Unmanned Aircraft Systems (sUAS) 

applications. Furthermore, low-power Automatic Dependent 

Surveillance – Broadcast (ADS-B) is evaluated for potential use to 

enable autonomous sUAS to sUAS deconflictions as well as to 

provide usable warnings for manned aircraft without saturating 

the frequency spectrum. 

Keywords—small unmanned aircraft systems (sUAS); sense and 

avoid (SAA); detect and avoid (DAA); unmanned aerial vehicle 

(UAV); Automatic Dependent Surveillance – Broadcast (ADS-B) 

I. INTRODUCTION 

Commercial applications of unmanned aircraft operating at 

low altitudes are likely to increase in the near future presenting 

both business incentives as well as huge airspace integration 

challenges. The full range of these low-altitude Unmanned 

Aircraft Systems (UAS) operations will likely include [1] 

“...those that are fully contained in uncontrolled airspace, to 

those that require transit across the boundary between 

controlled and uncontrolled airspace, and finally to those that 

originate and operate within controlled airspace ...” As a result, 

scenarios in which UAS will operate in close proximity to each 

other or with other users of the airspace will be increasingly 

common, such as in the vicinity of a terminal area. 

Swee Balachandran 

National Institute of Aerospace 

Hampton, VA, USA 

NASA Langley Research Center 

Louis Glaab, César Muñoz 

Hampton, VA, USA 

Consequently, the ability of small UAS vehicles to sense 

traffic aircraft in the airspace and maintain a safe separation 

distance from other vehicles is a fundamental requirement to the 

integration of UAS into the National Airspace System. Research 

on sense and avoid (also referred to as detect and avoid or DAA) 

for small UAS has focused mostly on development of separation 

assurance algorithms. Published research provides very little or 

no validation of these systems in a real-life setting. Furthermore, 

available sense and avoid algorithms for small UAS are often 

geared towards providing traffic awareness to remote pilots to 

maintain well clear, but do not consider the post-conflict 

maneuvers required to guide the vehicle back to the original 

path. 

This paper details the results of various tests (both simulation 

and flight tests) conducted to evaluate and validate the sense and 

avoid capability of the Independent Configurable Architecture 

for Reliable Operations of Unmanned Systems (ICAROUS). 

ICAROUS was designed as a distributed publish-subscribe 

software architecture to enable the easy integration of missionspecific functionality and/or sensor technologies. Presently, 

ICAROUS runs onboard the vehicle on a companion computer 

but future instantiations of ICAROUS could be directly 

integrated with lower-level autopilot functions. Simulation 

testing enabled performance evaluation against a wide variety of 

well-clear parameters, initial conditions, and encounter 

geometries. Flight testing was used to validate simulation results 

and demonstrate ICAROUS as a practical, usable system for real 

world UAS applications. Flight tests were conducted against an 

unmanned fixed wing aircraft to validate scenarios involving 

low closure rate encounters with other unmanned aircraft 

vehicles. A manned general aviation (GA) aircraft was used to 

validate the sense and avoid capability in a high closure rate 

scenario representative of encounters between UAS and GA 

aircraft in a terminal area setting. 

Two additional goals of this effort were to evaluate the 

efficacy of a representative Automatic Dependent SurveillanceBroadcast (ADS-B) receiver for sUAS as a source of 

cooperative traffic surveillance for Sense and Avoid (SAA) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

applications and to investigate the use of reduced-power ADSB position report transmissions for low-altitude sUAS 

operations. While the widespread use of ADS-B is not 

considered feasible for sUAS due to potential frequency 

congestion, low power ADS-B has the potential to enable these 

operations without negatively impacting the air traffic system. 

To be a viable approach, ADS-B output power must be 

significantly reduced while maintaining the range and quality 

needed for small UAS operations in addition to providing useful 

warnings to manned aircraft. If the use of ADS-B does not 

provide useful alerts to manned aircraft, then there would be no 

justification for its use for UAS. Multiple levels of attenuated 

ADS-B output were tested during this effort with both sUAS to 

sUAS as well as sUAS to manned aircraft to evaluate low power 

ADS-B as a solution to this problem. 

This paper is organized as follows. Section II provides an 

overview of existing research related to sense and avoid. Section 

III provides background on the ICAROUS architecture and the 

Detect and Avoid Alerting Logic for Unmanned Systems 

(DAIDALUS), which is used by ICAROUS. This section also 

includes background on the Minimum Operational Performance 

Standards (MOPS) used to evaluate ICAROUS maneuvers and 

on the sensors used by UAS in sense and avoid (SAA) 

operations. Section IV discusses methods used for simulation 

and testing of ICAROUS. Section V provides a detailed analysis 

of ICAROUS’ performance with varied input parameters, and of 

the effectiveness of low power ADS-B. Finally, sections VI-VII 

provide a discussion of observed results, future work, and 

conclusions. 

II. RELATED WORK 

Collision avoidance for manned aircraft has been an area of 

intense research since the advent of the Traffic Collision 

Avoidance System (TCAS). The TCAS system was developed 

to provide pilots with adequate information to make decisions 

regarding evasive maneuvers to mitigate risk due to an intruder 

in the airspace [2]. The TCAS system has seen several iterations 

and continues to be the backbone of collision avoidance in the 

civil commercial aviation community. Using transponders, 

TCAS I provides warnings (traffic advisories) of nearby 

intruders in the airspace. The TCAS II system also provides 

resolution advisories to the pilot in addition to the traffic 

advisories. In case of a resolution advisory, the pilot has the final 

authority and is required to implement these resolutions. 

The Advanced Collision Avoidance System (ACAS-X) [3], 

the next generation of collision avoidance algorithms, was 

introduced with the objective of replacing TCAS. Unlike TCAS, 

ACAS-X uses a model-based decision-theoretic framework 

where traffic resolutions and advisories are optimized using a 

reward function that considers the encounter dynamics. The use 

of a decision-theoretic framework introduces multi-dimensional 

lookup tables thus making its implementation as well as its 

verification and validation a challenging task. Verification and 

validation of ACAS-X resolutions is an ongoing research 

activity [4]. 

Unlike manned aircraft operating at high altitudes, small 

UAS have different mission dependent performance constraints. 

Consequently, a straightforward translation of the collision 

avoidance algorithms used for manned aircraft may not be 

applicable. Collision avoidance algorithms for small UAS need 

to be cognizant of various constraints such as geofences and 

obstacles in a low altitude airspace or an urban airspace 

environment. Integration of detect and avoid capability with 

path planning capability was discussed in [5]. 

For UAS, the final report of the Federal Aviation 

Administration (FAA) Sense and Avoid (SAA) Workshop [6] 

defines the concept of sense and avoid as “the capability of a 

UAS to remain well clear from and avoid collisions with other 

airborne traffic.” Based on this definition, the UAS Sense and 

Avoid Science and Research Panel (SARP) made a 

recommendation for a quantitative definition of UAS well clear 

that uses distance and time functions similar to those used in the 

TCAS II resolution advisory logic [7]. For large, remotely 

piloted UAS, the RTCA Special Committee 228 (SC-228) has 

developed minimum operational requirements for detect and 

avoid that uses SARP’s well-clear definition [8]. DAIDALUS 

[9] is a NASA developed software that serves as a reference 

implementation of the minimum operational performance 

standards for the UAS detect and avoid concept defined in 

RTCA FAA DO-365. The scope of the DAA requirements 

detailed in DO-365 is limited to large vehicles operating in 

controlled airspace. These requirements are not adequate for 

small sUAS operating in low altitude airspace. 

Several researchers have investigated the sensing aspects of 

collision avoidance for small UAS. Surveillance performance of 

sensors required to perform SAA in small UAS with emphasis 

on risk was considered in [10]. That work provides a mapping 

between surveillance performance and collision risk. An 

analysis of the usage of radars for sense and avoid was provided 

in [11]. The use of ground-based radars to perform sense and 

avoid in a local area was investigated in [12]. Dolph et al. [13] 

used cameras to visually detect intruders in the vicinity. 

Acoustic sensors for intruder detection have also been used in 

[14]. Experimental evaluation of a rudimentary sense avoid 

algorithm was conducted in [15]. A survey of various sense and 

avoid algorithms for small UAS can be found in [16]. 

Figure 1. ICAROUS architecture 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

III. BACKGROUND 

A. ICAROUS 

ICAROUS is an architecture that integrates a collection of 

core algorithms for path planning, geofence handling, traffic 

avoidance, and decision-making capabilities to enable 

autonomous operation of UAS [17,18]. Fig. 1 provides an 

overview of the ICAROUS architecture, which uses NASA’s 

core Flight Systems (cFS) middleware. ICAROUS was designed 

to run on a companion computer while consuming data from an 

autopilot system. It monitors the autopilot system, mission 

performance, and other mission and safety constraints. 

ICAROUS assumes control of the vehicle when a constraint 

violation is imminent and autonomously implements resolution 

maneuvers to prevent conflicts and mitigate risk. 

B. DAIDALUS 

The sense and avoid functionality implemented within 

ICAROUS is provided by DAIDALUS [9]. DAIDALUS is a 

DAA software library available under NASA’s Open Source 

Agreement. DAIDALUS consists of algorithms that predict 

well-clear violations between the ownship and traffic aircraft 

and provide maneuver guidance in the form of ranges of 

maneuvers for the ownship to maintain or regain well clear. 

These algorithms have been formally verified for logical 

correctness in the Prototype Verification System (PVS). 

C. ICAROUS Resolutions for SAA 

The DAIDALUS detect and avoid library is highly 

configurable and uses a list of parameters that govern its 

response to an intruder in the airspace. The present work 

evaluates only the lateral resolutions provided by DAIDALUS. 

These lateral resolutions are in the form of track guidance and 

require the ownship to change its heading, but not its altitude. 

Given the position and velocity of intruder aircraft in the 

airspace, DAIDALUS outputs a range of track angles that could 

result in well-clear violation. These outputs are a function of the 

initial parameter set used to configure DAIDALUS. A list of all 

parameters used by DAIDALUS and detailed explanation of 

these parameters can be found in [18] For the ISAAC test, the 

effects of the following parameters were investigated. 

• Well-clear alerting time threshold – time before 

predicted well-clear violation to start avoidance 

maneuver 

• Well-clear distance threshold (DTHR) – horizontal 

distance to be maintained between ownship and traffic 

aircraft 

• Well-clear time threshold (TTHR) – This time threshold 

is related to the Modified Tau (TAUMOD) threshold 

used in the TCAS alerting logic and it provides an 

estimate to time of closest point of approach. 

The time when a resolution maneuver is computed depends 

both on the alerting time threshold and the well-clear thresholds. 

The sum of these time thresholds approximate the time prior to 

closest point of approach when the resolution maneuver is 

computed. In the present work, these times are varied in a way 

that their sum remains constants. 

When a conflict occurs, ICAROUS selects an avoidance 

maneuver from the guidance maneuver ranges computed by 

DAIDALUS and autonomously commands the autopilot to 

execute it. ICAROUS also constantly checks to see if the turn to 

intercept the original flight plan would cross a conflict track 

heading. ICAROUS initiates the return to path maneuver only 

when the vehicle is clear of a well-clear conflict and when 

returning to the original mission no longer results in loss of 

separation. Return to path can be implemented in multiple ways 

depending on the mission. The vehicle could return to the next 

waypoint in the current flight plan or, alternately, it could return 

to the point on the flight plan where it initially deviated to avoid 

loss of separation. For this work, return to path is treated as 

returning to the next waypoint in the current flight plan. 

D. Sensors for SAA in Small UAS 

The limited payload carrying capability of small UAS poses 

significant restrictions on the type of onboard traffic surveillance 

sensors that can be used for SAA. Currently available off-theshelf sensors suitable for SAA applications on small UAS 

include vehicle-to-vehicle communication devices and ADS-B 

for cooperative traffic, and airborne radars, LIDAR, visionbased sensors (cameras), and acoustic sensors for noncooperative traffic. Ground based radars can also be used as a 

traffic surveillance source. Different sensor technologies have 

inherent capabilities and limitations, as well as varying 

performance metrics and operational constraints. ICAROUS 

was designed to be sensor agnostic, and configurable to adjust 

to different sensor performance and uncertainty ranges. Prior 

work conducted as part of the UTM Technology Capability 

Level 3 (TCL3) flight tests studied the suitability of vehicle to 

vehicle communication devices for sense and avoid [19]. 

This work specifically focuses on the use of the pingRX 

ADS-B receiver manufactured by uAvionix [20] for providing 

intruder position data (UAS-UAS and GA-UAS). The ping 

module is very light weight and integrates with a 

Pixhawk/ArduPilot flight controller [21]. 

The pingRX is considered to be representative of the types 

of ADS-B receivers that could be used for sUAS applications. 

Internal vehicle communication of ADS-B data is sent via the 

ADSB_VEHICLE message of the MavLINK protocol [22] 

containing information about aircraft call sign, ICAO address, 

GPS coordinates (latitude and longitude), altitude, and vehicle 

velocity. The ISAAC test relies on ADS-B technology to assess 

the performance of ICAROUS autonomous SAA capability 

since its performance and accuracy exceed those of other sensor 

technologies. However, the of use ADS-B-based SAA for sUAS 

is usually not considered to be an acceptable solution since the 

increased volume of transmissions could overload and impair 

existing systems. M. Guterres et al. [24] found that the main 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

factors that would impact ADS-B functionality are UAS fleet 

density and transmission power. To allow UAS fleet density to 

increase, reduced power ADS-B must be explored or another 

frequency spectrum be allocated for UAS use. Much research 

remains to be done to understand the potential applicability of 

low-power ADS-B for sUAS and its impact on the air traffic 

system. 

IV. METHOD 

A software in the loop (SITL) simulation capability was 

developed to study performance metrics of a large number of 

SAA configurations. The SITL integrates ICAROUS with a 

high-fidelity quad-rotor simulation model and an autopilot 

system through a MavLINK protocol. ICAROUS guidance 

commands are executed by the SITL simulator and relevant 

metrics are computed to characterize its performance. A wide 

range of SAA configurations were run, including well-clear 

distance thresholds ranging from 300 to 2000 feet, and alerting 

time thresholds ranging from 0 to 25 seconds. Preliminary 

analysis of the results was used to guide and down select the 

SAA configurations used in the ISAAC flight test. Flight test 

results validate simulation tests and further characterize the 

performance of the algorithms with real sensor data, winds, and 

turbulence, amidst various factors such as transmission latency, 

ADS-B packet drop outs and GPS errors 

A. Flight Test Approach 

A DJI S1000 octocopter with an ArduPilot autopilot system 

was used as the ICAROUS equipped ownship aircraft for these 

flight test. An onboard companion computer connected to the 

autopilot hosted the ICAROUS software that communicated 

with the autopilot via the MavLINK protocol. This setup is 

illustrated in Fig. 2. 

Figure 2. Hardware setup 

The flight test was conducted in two phases. For the first 

phase, UAS-to-UAS encounters, the intruder aircraft was a 

Tempest Unmanned Aerial Vehicle (UAV) [23]. For the second 

phase, UAS-to-GA aircraft encounters, the intruder was a Cirrus 

SR-22. Scenarios included encounters with two different 

geometries, head-on and at 90-degree angle. All flight tests were 

conducted at the Beaver Dam Air park located near Smithfield, 

VA, in the vicinity of multiple airports. As a result, ADS-B 

signals from multiple commercial planes were also present 

during testing. 

Flight safety requirements imposed several constraints on 

the encounter geometry whose impact warrants an explanation. 

Since both UAS had to be within visual-line-of-sight of the 

operators at all times, it was challenging to set up the test 

conditions with larger well clear distances and with encounters 

with high closure rate (as those involving a GA). Since in those 

cases the encounters start when the aircraft are in close 

proximity to the well-clear boundary, late detections are likely 

to occur. In those cases, the ownship may not have enough time 

to avoid the specified well-clear. 

1) Flight Test Setup 

The flight test comprised two phases: Phase 1 included UAS vs 

UAS encounters and Phase 2 UAS vs GA aircraft encounters. 

a) Phase 1: UAS vs UAS 

Fig. 3 illustrates the flight plans of the two aircraft for a head 

on encounter. On the left (shown in blue) is the flight path of the 

fixed-wing intruder (Tempest) aircraft. On the right (shown in 

yellow), is the flight path of the ownship (DJI S1000) vehicle. 

The intruder aircraft flew counter clockwise flights which 

generated a direct head-on collision hazard for the ownship. A 

500 foot boundary was established to separate the vehicles and 

mitigate risks of mid-air collisions. 

Figure 3. Head on encounter UAV flight plans 

These flight paths are chosen so that the vehicles are 500 feet 

apart at the closest point if no autonomous traffic deconfliction 

maneuvering is performed. The two vehicles are set up to 

operate at roughly the same altitude. The intruder aircraft 

maintains a constant speed of 20 m/s throughout all encounters. 

The ownship maintains a constant speed of 10 m/s. Thus, the 

relative closure rate between the two vehicles for the head on 

encounters is 30 m/s (approximately 60 knots). This rate of 

closure is considered to be representative of nominal sUAS to 

sUAS encounters, but higher closure rates may be possible. 

Flight paths are chosen to maximize the likelihood of a predicted 

conflict between the two vehicles. In Fig. 3, a well-clear conflict 

should occur between the two vehicles during the southernmost 

leg of the flight plan. 

Flight paths for the 90-degree encounter are shown in Fig. 4. 

Once airborne, the Tempest intruder aircraft is flown up to its 

cruising altitude and its autopilot is engaged so that it continues 

to fly the programmed rectangular pattern. After the Tempest is 

established on its flight plan, the octocopter is launched and 

manually flown to waypoint X1. Once at waypoint X1, the 

octocopter loiters until the encounter can be initiated. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The encounter is initiated by engaging the Pixhawk's 

autopilot, triggering it to fly the programmed flight plan. An 

Figure 4. 90-degree encounter UAV flight plans 

encounter initiated too soon can result in the octocopter 

completing the flight leg between waypoints X1 and X2 before 

observing a well-clear conflict. Similarly, an encounter initiated 

too late can result in the intruder completing its leg ahead of the 

ownship or the traffic deconfliction maneuver being initiated 

prior to reaching steady-state cruise conditions. An encounter 

initiated at the right timing results in the two vehicles 

approaching each other head on, leading to a well-clear conflict. 

Several trials were done to establish the timing to produce a well 

clear. The distance of the intruder from waypoint Y1 can be used 

to time encounters consistently. The exact timing depends on the 

well-clear radius, alerting time of the detect and avoid 

configurations used by ICAROUS, and prevailing winds and 

needs to be adjusted throughout testing. Fig. 5 shows an example 

of detect and avoid response from a head on encounter. 

Figure 5. Example of DAA maneuver 

The octocopter's autopilot is engaged at point X1, when the 

corresponding position of the intruder is near point Y1. Once the 

ownship's autopilot is engaged, it starts accelerating towards its 

next waypoint, X2. At point t1 a well-clear conflict is predicted 

and ICAROUS assumes control of the ownship's autopilot. 

ICAROUS issues guidance commands to direct the ownship 

away from the well-clear conflict. ICAROUS continues this 

heading guidance until it is safe to return to the mission. The 

computation of the return to path maneuver is discussed in 

Section III. At point t3, the ownship is safe to return to the 

original mission and ICAROUS starts guiding the aircraft 

towards the next waypoint, X2. 

b) UAS vs GA 

The flight paths for the manned aircraft are shown in Fig. 6. 

Note that for tests involving a well-clear radius of 2000 feet, the 

flight path of the GA aircraft is offset by 1000 feet. This is 

mainly to restrict the deviations of the UAS to be within 1000 

feet in the pilot's line of sight. In these tests, the GA aircraft 

maintains a constant airspeed of 100 knots. The ownship 

maintains a constant speed of 10 m/s. Note that this setup results 

in a relative closure rate of 60 m/s in head-on encounters. Timing 

for encounter initiation is similar to the flight tests with a UAV 

intruder. Following the GA plane position on an ADS-B monitor 

helps to get the timing right. 

Figure 6. Flight plans for manned intruder 

2) Tests with Geofences 

Flight tests also included scenarios with a simple keep-out 

geofence added either to the north or south of the ownship's 

flight plan to force deviations in specific directions. These 

geofences can be static, i.e. known before flight, or dynamic, i.e., 

provided during flight (also referred to as “dynamic 

restrictions”). In this setup, the ownship is expected to deviate 

away from the geofence while executing a traffic avoidance 

maneuver. These additional conditions were included to 

demonstrate ICAROUS integrated SAA-geofence containment 

capability. 

3) Reduced Power ADS-B 

During the flight tests described above, three levels of UAS 

ADS-B output power were tested: 

• 40 Watts (full power) 

• 1.3 Watts (15 decibel attenuation) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

• 0.4 Watts (20 decibel attenuation) 

The different output power levels were produced by adding 

a 50 Watt RF attenuator to the ADS-B transmitter on the UAV 

intruder (Tempest). By swapping out the attenuator between 

flights, different transmission powers were achieved. The output 

power for each scenario is estimated based on the 40 Watts 

nominal output of the antenna. 

B. Flight Test Conditions 

The ISAAC test conditions were designed to evaluate the 

impact of different well-clear volumes and alerting times used 

by the core avoidance logic. Table 1 enumerates the test 

conditions for both the UAS-UAS and UAS-GA encounters. 

TABLE 1. TEST CONDITIONS 

Intruder 

Type DTHR 

Alerting 

Time (s) 

TTHR 

(s) 

Encounter 

Type Runs 

GA 1000 20 0 Head On 3 

GA 1000 10 10 Head On 2 

GA 1000 20 0 90 deg 1 

GA 1000 10 10 90 deg 3 

GA 2000 20 0 Head On 2 

GA 2000 10 10 Head On 3 

GA 2000 0 20 Head On 3 

GA 2000 20 0 90 deg 3 

GA 2000 10 10 90 deg 3 

GA 2000 0 20 90 deg 3 

UAS 1000 20 0 Head On 6 

UAS 1000 10 10 Head On 2 

UAS 1000 0 20 90 deg 3 

UAS 2000 10 10 90 deg 5 

Each condition is repeated several times (runs) for different 

well-clear configurations. As explained earlier, the encounter 

geometries were constrained by the flight safety requirements as 

well as the test site limits. Hence, only 1000 and 2000 feet of 

horizontal separation were tested. Also, at most 20 seconds of 

total alerting time + TTHR time were tested. The choice of 

alerting time (i.e., time at which maneuver guidance is first 

computed prior to well-clear violation) and TTHR (i.e., wellclear time threshold based on aircraft closure rate) values was 

informed by simulation results. 

Only lateral avoidance maneuvers are considered for this 

effort. Lateral maneuvers require the ownship to change 

heading, but not altitude, to maintain separation with the 

intruders. In both phases of flight testing, the intruders do not 

react to the ownship and continue on their respective flight plans. 

Additional maneuvers are being explored as part of ongoing 

work. 

Fig. 7 summarizes the impact alerting time, well-clear 

radius, and time threshold on the horizontal miss distance at the 

closest point of approach during the different encounters. 

In the graphs, a horizontal miss distance smaller than the 

horizontal well-clear radius indicates a loss of separation, while 

a separation distance greater than the well-clear radius indicates 

a conservative avoidance maneuver. 

V. RESULTS 

A. ICAROUS Performance 

Figure 7. Horizontal Miss Distance for all Horizontal Distance, Alerting 

and Threshold time test parameters 

There were 42 successful runs, 16 UAS-UAS and 26 UASGA encounters. The UAS-UAS encounters experienced no 

losses of separation regardless of horizontal separation or 

combination of alerting and threshold time parameters used. 

This may indicate that for low closing speeds (< 30 m/s) the 

configurations chosen are safe and possible overly conservative. 

Of the 26 UAS-GA encounters there were 16 losses of 

separation, with intrusion distances ranging from 14 feet to 1000 

feet. The data shows that all but 1 of the losses was related to the 

test site constraints and the choice of parameters that proved to 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

be insufficient for the closing speeds of the GA-UAS 

encounters. In those cases, the SAA equipped UAS was 

initialized too close to the well clear boundary or in some cases, 

already in a loss of separation. However, the data seems to 

indicate that the alerting time component is more consequential 

than TTHR resulting in fewer losses of separation. The most 

successful set of runs corresponds to 2000 feet of horizontal 

separation and 20 seconds of alerting time. 

Analysis of the remaining case shows an anomaly in the 

system, possibly related to an autopilot condition that caused the 

UAS to slow down as the encounter progressed. After the UAS 

reaches the commanded speed of 10 m/s in autonomous mode, 

it unexpectedly and abnormally decreases the speed to 0.53 m/s. 

The GA aircraft is flying in the direction of the UAV at a speed 

of 54.5 m/s (106 knots). According to configuration parameters, 

maneuver guidance is computed by ICAROUS 21 seconds prior 

to CPA when the aircraft have a horizontal separation of 3763 

feet (1147m). Given the performance limits of the UAS, 

ICAROUS does not find a track resolution for the UAS. It does 

find ground speed and vertical resolutions, but they are not 

currently integrated into the decision-making logic. The UAS, 

still in autonomous mode, slowly accelerates. The first track 

resolution autonomously implemented by the UAS is computed 

by ICAROUS 16 seconds prior to CPA. At this time the aircraft 

have a separation of 2827.73 feet (862m). The UAS does not 

have enough time to avoid a loss of separation and passes 758.68 

ft (231 m) from the GA aircraft. 

B. ADS-B Sensor Performance 

1) ADS-B for UAS Sense and Avoid 

The pingRX ADS-B receiver used in these tests provides a 

simple practical way to incorporate ADS-B data into UAS sense 

and avoid operations and is considered to be representative of 

future devices that could be used for commercial UAS 

operations. In our tests, the pingRX was able to successfully 

decode greater than 90% of the ADS-B messages sent by the 

intruder UAV aircraft at full transmission power (40 W). 

Slightly better results were seen from the GA plane, with greater 

than 95% of ADS-B messages successfully received. Since this 

analysis was performed using telemetry logs from the ownship 

ground station, some of these missed updates could be attributed 

to lost packets in the telemetry link, which would not affect the 

amount of ADS-B data available to ICAROUS. However, 

analysis shows that fewer than 2% of packets were dropped over 

the telemetry link. In addition, packets lost due to drops out of 

the telemetry link would be assumed to affect all ADS-B 

transmission power levels and aircraft types (UAS vs GA). 

When using the pingRX, parameters such as ADS-B range 

filter and maximum number of ADS-B targets passed on to the 

autopilot system need to be carefully selected for each 

application. Otherwise, messages from the target intruder 

aircraft may be dropped due to the pingRX being overloaded 

with aircraft in the general vicinity. During testing it was 

observed that the pingRX was receiving ADS-B data from 

aircraft more than 50 miles away. The pingRX also performed 

less reliably when subjected to large vibrations in flight. Periods 

of high vibration appeared to coincide with gaps in ADS-B 

reception of up to 10 seconds. These issues should be understood 

and accounted for before using ADS-B and the pingRX for 

safety critical UAS operations. 

ICAROUS performed successful SAA maneuvers using 

ADS-B input from the pingRX for more than 30 flight tested 

encounters. The range of full power ADS-B has no effect on 

small UAS operations, as the lookahead time and alerting time 

parameters in ICAROUS limit the monitored range to be much 

smaller than the actual range of ADS-B reception. The few 

encounters where ICAROUS failed to maintain well clear were 

not caused by ADS-B errors, but were due to well-clear 

definition and timing parameters that were inappropriate for the 

scenario being tested. For example, alerting time was too small 

to alert ICAROUS of an incoming aircraft with enough time to 

complete the required maneuver. 

These tests demonstrate the use of ADS-B for sUAS in 

scenarios involving both unmanned and manned aircraft. Any 

ADS-B receiver needs to be carefully integrated and tested 

before use in safety critical applications. 

Figure 8. Probability of pingRX successfully receiving a 0.4 Watt output 

ADS-B message, dependence on separation range 

2) Reduced Power ADS-B 

ICAROUS performed successful detect and avoid 

maneuvers at three different ADS-B output power levels. Even 

with the lowest power (0.4 Watt) signal, 83% of ADS-B 

messages were received by the pingRX unit within the 0-1200 

meters separation range tested. This reception rate proved 

sufficient for ICAROUS avoidance maneuvers within this 

range. 

Fig. 8 shows a logistic regression between the probability of 

ADS-B message reception and separation range between the two 

vehicles. The regression shows how increasing distance between 

the two vehicles reduces the probability that a transmitted ADSB message will be successfully received by the ownship. Each 

received message is associated with the distance between the 

two vehicles at the time the message was sent, based on ownship 

GPS position and intruder ADS-B data. The missed messages 

are estimated based on the standard once per second broadcast 

rate, and associated with the approximate distance between the 

two vehicles at the time of expected transmission. For the full 

power signal, the range between vehicles does not significantly 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

affect the probability of message reception (p = 0.125 > 0.05) 

within the tested range. For the 1.3 Watt signal, the range 

significantly affects the probability of message reception (p = 

0.015 < 0.05). For the 0.4 Watt attenuated signal, the effect is 

highly significant (p < 0.0001 < 0.05). This is expected for 

reduced power transmissions. 

A 70% reception corresponds to an expected 2-3 seconds 

between messages. As the vehicles get closer together, this 

reception rate increases. These tests indicate that these message 

reception rates are sufficient for UAS avoidance maneuvers, 

even at the greatly reduced transmitting power. M. Guterres et 

al. [24] concluded that UAV ADS-B transmit power within 

0.01-0.1 Watts, coupled with a fleet density less than 5 UAVs 

per square kilometer is required to maintain acceptable ATM 

message reception rates of greater than 80%. The current test 

evaluated only single aircraft and testing of multiple aircraft in 

higher-density operations is required to establish the actual 

upper power limit. The lowest output power tested in this effort 

was 0.4 Watts which is above the suggested criteria yet provides 

minimum required performance. It is feasible that the sensitivity 

of the pingRX unit could be improved, but that was beyond the 

scope of the current effort. Lower transmission powers that fall 

within this range should be tested in future efforts. In addition to 

achieving adequate performance for UAS to UAS applications, 

results for UAS to GA aircraft were also adequate. Observations 

from the manned GA SR-22 aircraft revealed that for the midpower level the position of the intruder Tempest aircraft was 

observed more than 15 miles away. When the Tempest ADS-B 

power output was attenuated to ~400 mWatts, its location was 

observed consistently by the GA aircraft from at least 8 miles 

away. This distance is equivalent to approximately 4 minutes 

warning time for the GA aircraft. 

VI. DISCUSSION 

A successful maneuver to avoid a well-clear conflict is 

largely a function of the performance of the aircraft and position 

communication link. Factors such as the speed and turn rate of 

the vehicle play a significant role in maneuvering to avoid an 

approaching intruder and also affect the required range of 

position communication link performance. Consequently, 

careful consideration of the parameters chosen for well clear is 

crucial. These parameters must be chosen to handle the fastest 

intruder in a given airspace. 

The phase 1 flight tests with the Tempest aircraft resulted in 

closure rates of 30 m/s during head on encounters and 22 m/s 

during the 90-degree encounters. With a turn rate of 10 

degrees/second, and a top speed of 10 m/s, the octocopter was 

able to maneuver fast enough to avoid a well-clear violation 

defined by the configuration parameters. 

Proper selection of parameters depends on many factors 

including intruder speed, desired separation radius, and ownship 

limitations in turn rate and speed. Conservatively chosen 

parameters will reduce the risk of a well-clear violation, but 

consume more time and fuel as the ownship executes long 

maneuvers that are not necessarily required to maintain safe 

separation. On the other hand, if parameters are chosen to 

maximize efficiency, the ownship may lose well clear during 

encounters with higher speed vehicles. A well-clear definition 

and parameters must be chosen carefully for each application 

and be based on actual assessments of the risk of vehicle 

collisions. Values evaluated herein were considered nominal and 

appeared very conservative to ground observers. The results of 

these tests characterize ICAROUS’ performance under different 

configurations and inform the effective selection of parameters 

for future operations. 

VII. CONCLUSIONS AND FUTURE WORK 

A. Development of ICAROUS 

The ICAROUS capability for autonomous SAA was 

designed as a distributed software architecture based on wellestablished communication protocols and developed using 

formally verified SAA algorithms. ICAROUS has been 

undergoing extensive simulation evaluations as well as flights to 

evaluate its performance in real flight conditions. 

The ISAAC flight test was designed to explore the 

performance of the autonomous SAA functionality in conflict 

encounters of a UAS with both another UAS and a GA aircraft. 

The flights were conducted on days with mild wind conditions. 

Performance was measured in terms of horizontal miss distance 

as a function of well clear horizontal separation, alerting, and 

threshold times. 

Results from this test indicate that ICAROUS autonomous 

SAA capability was very effective in maintaining separation 

between two UAS in mild wind conditions, with closing speeds 

not exceeding 30 m/s, and for all combination of alerting and 

threshold times and distance parameters used. Results from the 

UAS-GA encounters were impacted by the constraints of the 

ISAAC test design that did not allow some of the test conditions 

to be initialized properly, causing late detections and losses of 

separation. However, within the limits of the test conditions, 

results seem to indicate that at closing speeds in the order of 100 

knots an alerting time of 20 sec and a separation threshold of 

2000 feet was effective for all tested encounters. 

The ISAAC test was the first flight test designed to shed light 

over this complex and wide-ranging problem and, clearly, there 

remains work to be done. Future flight studies need to explore 

the parameter space beyond the ISAAC limits and to 

characterize the performance of other sensor technologies for 

SAA to determine if it can support such applications. 

B. Attenuated ADS-B Output for UAVs 

These tests demonstrate that low powered, 0.4 Watt, ADS-B 

transmission is a practical option for UAV to UAV applications. 

The output powers tested provided sufficient quality and range 

for ICAROUS to perform consistent detect and avoid 

encounters. Reduced power should decrease the risk of 

frequency congestion that would interrupt normal ADS-B use, 

but may require lower transmit powers than tested here. In 

addition, further work is needed to understand the potential 

impact of high density low-power transmissions on ground 

receivers and multilateration systems. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Future tests should assess the 0.01 to 0.1 Watt transmission 

power suggested by [24]. Tests should also be conducted over 

larger separation distances and examine the impact that several 

UAVs transmitting at this low power would have on ATM ADSB operations, especially the impact of a fleet of UAVs operating 

at or near an airport. 

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Forum, 2017. 

AUTHORS BIOGRAPHIES 

Maria Consiglio is a senior research engineer at NASA 

Langley Research Center. Ms. Consiglio work in air traffic 

management, autonomy and sense and avoid research at NASA 

spans two decades and over 30 publications. She currently 

serves as the UTM Associate Project Manager and the technical 

lead for the ICAROUS research team. 

Brendan Duffy is a research engineer at the National Institute 

of Aerospace, supporting UAS research in the Safety-Critical 

Avionics Systems Branch at NASA Langley Research Center. 

He holds a B.S. in Mechanical Engineering from Cornell 

University. 

Dr. Swee Balachandran got his PhD in Aerospace 

Engineering from the University of Michigan, Ann Arbor in 

2016. He is currently a researcher with the National Institute of 

Aerospace. Dr. Balachandran's current research focuses on the 

development of formally verified algorithms for autonomous 

systems. 

Lou Glaab is the Assistant Branch Head for the Aeronautics 

System Engineering Branch at NASA Langley Research Center. 

Lou has worked in the areas of aerodynamics, synthetic vision 

systems, Entry Descent and Landing, and Unmanned Aerial 

Systems. Recent work involves contributions to the UAS 

Traffic Management project. 

Dr. Cesar Munoz is a senior research computer scientist 

at NASA Langley Research Center since 2009. His primary 

research interest is the development of formal methods 

technology for the design, verification, and implementation of 

safety-critical aerospace systems. Dr. Munoz got his Ph.D. 

degree in Computer Science from University of Paris 7, 

France, in 1997