Development of a Low-Cost Sub-Scale Aircraft for Flight 

Research: The FASER Project 

D. Bruce Owens*, David E. Cox†, and Eugene A. Morelli‡

NASA Langley Research Center, Hampton, VA, 23681 

An inexpensive unmanned sub-scale aircraft was developed to conduct frequent flight 

test experiments for research and demonstration of advanced dynamic modeling and control 

design concepts. This paper describes the aircraft, flight systems, flight operations, and data 

compatibility including details of some practical problems encountered and the solutions 

found. The aircraft, named Free-flying Aircraft for Sub-scale Experimental Research, or 

FASER, was outfitted with high-quality instrumentation to measure aircraft inputs and 

states, as well as vehicle health parameters. Flight data are stored onboard, but can also be 

telemetered to a ground station in real time for analysis. Commercial-off-the-shelf hardware 

and software were used as often as possible. The flight computer is based on the PC104 

platform, and runs xPC-Target software. Extensive wind tunnel testing was conducted with 

the same aircraft used for flight testing, and a six degree-of-freedom simulation with 

nonlinear aerodynamics was developed to support flight tests. Flight tests to date have been 

conducted to mature the flight operations, validate the instrumentation, and check the flight 

data for kinematic consistency. Data compatibility analysis showed that the flight data are 

accurate and consistent after corrections are made for estimated systematic instrumentation 

errors. 

Nomenclature 

α = angle of attack 

β = angle of sideslip 

θ = Euler pitch angle, deg.

c.g. = center of gravity 

CL = lift coefficient 

Cl = rolling moment coefficient 

Clβ = static lateral stability Cι ∂∂ β , per degree 

Cm = pitching moment coefficient 

Cn = yawing moment coefficient 

Cnβ = static directional stability ∂Cn ∂β , per degree 

V = freestream velocity, ft/sec 

ARF = Almost Ready to Fly 

COTS = Commercial off-the-shelf 

DOS = Disk Operating System 

FS = Full-scale 

GPS = Global Positioning System 

IMU = Inertial Measurement Unit 

INS = Inertial Navigation System 

ISA = Industry Standard Architecture (IBM PC bus standard) 

 

*

 Aerospace Engineer, Flight Dynamics Branch, M/S 308, Associate Fellow. 

†

 Senior Research Engineer, Dynamic Systems and Control Branch, M/S 308 

‡

 Aerospace Engineer, Dynamic Systems and Control Branch, M/S 308, Associate Fellow. 

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NTSC = National Television Systems Committee 

PC/104 = Miniature Form Factor Specification for IBM PC compatible computer 

PROM = Programmable Read-Only Memory 

PWM = Pulse Width Modulation 

RC = Radio Controlled 

RFI = Radio Frequency Interference 

RS232 = Serial Port communication standard 

TCP/IP = Ethernet communication standard 

I. Introduction 

Aeronautical research requires flight testing, both for 

validation of results obtained on the ground, and to guide the 

research. In general, the more complex the research issues, the 

more flight testing that must be done to get reliable answers. For 

some research, such as flight testing involving spins or other outof-control motion, or developing reconfigurable controls under 

failure conditions, it is impractical to use manned aircraft for flight 

research, because of the high cost and safety risks. Since sub-scale 

flying models are less expensive and unmanned, risks can be taken 

in research and development that could never be tolerated in a 

piloted flight test. There is also a need for an intermediate step 

between simulation and full-scale flight testing, particularly for 

nonlinear dynamic modeling and novel control designs. A subscale model aircraft can provide increased confidence in the 

methods and developments before risking the large investment 

ultimately necessary for a full-scale flight test demonstration. Subscale aircraft flight tests can be used early in the development 

phase to address these needs. 

In terms of funding, the trend in recent NASA spending for 

flight research and aeronautical research in general has been 

decreasing. To maintain a high degree of excellence in 

aeronautics research, this environment requires the development of 

low-cost flight research aircraft. NASA Langley Research Center (LaRC) has a long history of using sub-scale 

aircraft for flight research, and currently is heavily invested in using sub-scale aircraft for flight research1-3. The 

objective of the Free-flying Aircraft for Sub-scale Experimental Research, or FASER, project is to provide an 

inexpensive unmanned aerial vehicle to conduct frequent flight test experiments for developing and demonstrating 

advanced methods for experiment design, data analysis, dynamic modeling, and control law design. 

Table 1 FASER Geometric Characteristics

Fuselage

Length, ft 4.31 

Max. height, ft 0.4167 

Max. width, ft 0.3958 

Wing

Reference area ft2 8.28 

Span, ft 6.29 

Mean geometric chord, ft 1.42 

Leading edge sweep, deg. 0 

Aspect ratio 4.778 

Taper ratio 1 

Dihedral angle, deg. 0.7 

Twist, deg. 0 

Incidence at root, deg 0 

Propeller

Diameter, in 14.0 

Pitch 9.5 

Number of blades (folding) 2 

Figure 1 The FASER aircraft: Ultra Stick 120 with Ethernet connection of the laptop to the airplane shown. 

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II. Aircraft Characteristics 

Figure 2b Basic lateral-directional aerodynamics and 

static stability of FASER 

Figure 2a Basic longitudinal aerodynamics and static 

stability of FASER 

Unlike many NASA projects, FASER was not intended to emulate any specific aircraft. The project sought a 

conventional aircraft with acrobatic performance characteristics. To satisfy this requirement, and to stay consistent 

with keeping costs low, the Hanger 9 ARF Ultra-StickTM 120 kit-built tail-dragger was chosen, see Figure 1. This 

aircraft requires minimum build time, is inexpensive ($200 (airframe only), at the time of purchase), and is large 

enough to house the flight systems. Therefore, a lost 

airframe would not incur significant financial and 

schedule burdens. The geometric characteristics of the 

airplane are listed in Table 1. Airplane propulsion is 

provided by an electrically driven propeller. The 

geometric characteristics and electric propulsion were 

chosen to help meet the objectives of the project. 

The size of the aircraft was chosen so that the same 

aircraft could be flown and tested in the wind-tunnel at 

similar flight conditions. This approach eliminates 

scaling effects when comparing wind-tunnel and flight results. Proper Reynolds number and dynamic scaling 

parameters are hard to achieve, as described in Ref. 4. One of the objectives of the FASER project is to develop 

advanced aerodynamic modeling methods. Scaling effects plague the development of these methods when applying 

sub-scale model aerodynamics to full-scale aircraft4

. Extensive static wind-tunnel testing has been completed 

through a large range of angle of attack α and sideslip angle β, with and without power. In keeping with the 

advanced experiment design objective of the project, Ref. 5 reports on using modern design of experiments (MDOE) 

to conduct the first FASER wind-tunnel test. 

Results of the wind tunnel tests indicating the basic aerodynamics and static stability of the aircraft are shown in 

Fig. 2. The lift data in Fig. 2a shows two stall points, a minor one at α = 12° and the main one at α = 15°. To date 

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no attempt has been made to ascertain the cause in the double stall characteristic but the first stall is probably due to 

a laminar separation in the leading edge region. The second stall is probably full wing stall that results from flow 

separation progressing from the trailing edge forward as angle-of-attack increases. The pitching moment data shows 

that the model is statically stable through the stall angle-of-attack, having a stable break at the stall. The rolling and 

yawing moment curves in Fig. 2b show that at stall there is significant rolling and yawing moment input associated 

with the stall at α = 12°. This rolling and yawing moment input continues to increase through the full wing stall. 

Finally, the figure shows that the aircraft has static lateral stability up to α = 30° and is only slightly directionally 

unstable for 17° ≤ α ≤ 20°. In general, the data in figure 2 shows that the FASER aircraft has benign static 

aerodynamic characteristics. 

Another important size requirement is for the fuselage to be large enough to house the requisite flight systems. 

Initially, it appeared the selected model’s fuselage size would suffice. During the final integration of all the 

instruments and material needed to suppress the unplanned RFI problems, it was realized that a larger fuselage 

would have made integration easier. As is, the fuselage payload section is at the bare minimum for housing the 

electronics. 

Electric propulsion was chosen for a number of reasons. First, it is easier to conduct a power-on wind tunnel 

test using an electric motor. Second, using electric propulsion allows the mass properties of the model to remain 

constant during the entire flight. This simplifies the flight data analysis and modeling. Third, the electric propulsion 

system allows thrust to be turned off temporarily in flight, then switched back on. This expands the flight test 

capabilities to include identification of power effects. Finally, electric motors reduce the work load during flight 

operations by eliminating engine tuning and fuel handling. The selected motor has been very reliable and requires 

no maintenance. 

A major issue with electric motors is overheating. Bench and flight tests of several propulsion systems were 

conducted. The Aveox F-27 was used first but unfortunately this motor would quickly overheat. This caused 

significant problems during the first wind tunnel test. Direct drive outrunner motors were tested to address this 

problem. They were found to provide high torque without excessive heating. The Köhler Actro 40-4 electric motor 

using the Jetti Advanced 77 Opto brushless controller gave the best overall performance in terms of maximum 

endurance and thrust. Bench tests and flight tests of Nickel-Cadmium (NiCad) and Lithium-Polymer (LiPo) 

batteries were performed. The tests showed that the LiPo batteries provided more thrust and longer endurance than 

the NiCad batteries for less cost and weight per amp-hour. Also after the flight, gloves were required to remove the 

NiCad battery from the aircraft because it was so hot. The LiPo battery can easily be handled with the bare hand 

post-flight. The only disadvantage to using the LiPo batteries compared to the Ni-Cad is the higher potential for fire 

during charging and discharging. Also, special care must be given not to discharge the batteries below 3 volts per 

cell. Therefore, special circuitry that is inline between the receiver and motor controller measures the battery 

voltage and prevents the battery from being discharged below 3 volts per cell. The battery used to drive the Köhler 

motor comes from a 10 cell, 37 volt, 8 amp-hour LiPo battery. 

The mass characteristics of FASER were determined using the tri-filar pendulum method. Details of how the 

weight, balance, and inertias were determined are given in Ref. 7. The mass characteristics of individual 

components were determined so that if internal components were moved or switched out, it would be possible to 

determine the mass properties without having to re-swing the aircraft. The current mass characteristics of FASER 

are given in Table 2. 

Table 2 FASER Mass Properties

Weight 

(lbf) cg x c cg y b

cg z c

above the 

bottom of 

the fuselage 

at cg x c

x I

(slug-ft2

)

y I

(slug-ft2

)

zI

(slug-ft2

)

xz I

(slug-ft2

)

19.72 0.25 0 0.165 0.496 0.656 1.164 0.560 

A six-degree-of-freedom (6-DOF) simulation built in MATLAB® and Simulink® has been constructed to assist 

in the development of flight tests and control law design7,8. The simulation models the nonlinear aerodynamics 

using the wind-tunnel data collected to date and the damping derivatives from analytical methods. The simulation 

includes an engine thrust lag, but does not model servo dynamics. Flight tests to date have been done to assure that 

the instrumentation (see Table 3) is adequate to conduct high-quality flight dynamics and control research. Also, in 

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keeping with the advanced experimental design objective of FASER, damping derivative information was obtained 

from flight test, using a novel application of modern experiment design6

. 

III. Flight Operations 

Flight operations for FASER are conducted under a set of guidelines and approvals from the NASA Langley 

Research Center’s Airworthiness and Safety Review Board (ASRB) and a certificate of authority (COA) from the 

Federal Aviation Administration (FAA). FASER is flown at a number of FAA/ASRB approved flying fields local 

to NASA LaRC. Proposed flight research for the FASER project is annually reviewed by the ASRB in order to 

obtain a flight safety release. A partial requirement to obtain the flight safety release is having approved safety 

procedures for use during all phases of ground and flight operations. Safety procedures are required reading for all 

personnel involved in the flight operations. 

Flight operations are conducted in the following manner. At the flying field the model is assembled and 

functionality of all control surfaces and instruments are checked. Video and/or digital photographs are made of the 

aircraft for documentation in case of an incident. Then, an RFI check is made to assure that the onboard electronics 

have not fatally degraded the 72 MHz transmitter signal. When the aircraft is operationally ready, a safety briefing 

is conducted to inform personnel of the test objectives, crew member responsibilities, and emergency procedures. 

The flight test requires a pilot, and two flight test engineers, one of whom is acting as a spotter. One of the 

engineers is calling out the maneuver sequence from a flight card. The other flight test engineer records a stop 

watch time of the start of the maneuver, the flight card number, and any comments from the pilot and/or flight test 

engineer. The use of two flight test engineers assures complete and accurate documentation of the flight. In 

addition, the documentation greatly enhances the post-flight test analysis. Additional personnel are used 

occasionally for ground-based video recording of the flights. Currently, most data are stored onboard. After a 

flight, the aircraft is connected to a PC laptop for data download via Ethernet link. A quick review and analysis is 

made of the data to assure all instruments are working properly and for guidance in subsequent flight tests. 

IV. Flight Systems 

The flight systems consist of a flight control computer, vehicle instrumentation, and telemetry equipment. The 

flight computer has physical interfaces for serial communication, pulse width modulation, frequency measurement, 

and analog input and output (I/O). Radio frequency telemetry systems include direct ground pilot control, as well as 

ground data links and video transmission. Instrumentation includes a GPS based inertial navigation system, as well 

as analog measures of control surface position and wing mounted α/β/velocity probes. This section documents the 

equipment selection and configuration for each of these areas. 

Flight Computer: 

Several requirements were considered in developing the flight computer system. For dynamic modeling work, 

it was important that the system allow a significant amount of data storage and it was desirable to be able to generate 

coupled maneuvers at repeatable points in the flight envelope. For controls research, the flight computer should be 

easily programmable and have sufficient computational power to implement novel control strategies. Embedded 

microprocessors are desirable from a power and weight standpoint; however, they typically have limited storage 

capacity and require significant effort to reprogram – often with a requirement to implement algorithms in fixedpoint arithmetic. For ease of programming, processor capability, and cost, a PC/104 based system was selected for 

the flight computer. 

PC/104 is a set of mechanical and electrical specifications for a small form-factor PC/AT compatible computer, 

with the 16-bit ISA bus architecture still seen in desktop computers. Processors are Intel compatible 486/586 with 

clock speeds in the 100 MHz to 1 GHz range and use standard (laptop-style) dynamic memory. The lower clock 

speeds allow the PC/104 to run without a cooling fan. Although the clock speed is modest by current desktop 

standards, this provides significant computing power for real-time embedded applications and retains a historical 

compatibility which lowers the cost of interface cards both from a hardware and software driver perspective. This is 

important, as the flight computer will require interfaces to RS-232/422 serial streams, counters for pulse width 

modulation, and analog I/O, as well as network communications via TCP/IP. 

Although DOS, and even Windows®, are popular in PC/104 computers, several more appropriate real-time 

operating systems are available. Particularly attractive for this application was the xPC-Target toolbox from The 

Mathworks, Inc. This software provides a real-time operating system which will execute compiled Simulink®

diagrams and has block libraries that provide drivers for many PC/104 peripheral cards. Simulink® provides an easy 

environment to design and modify the real-time code. Since the driver blocks are constructed to run in the 

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simulation environment as well as the real-time environment, this also allows debugging and testing of algorithms 

on the desktop before deploying them to the embedded computer. This can reduce development and debugging time 

significantly. MATLAB® and Simulink® are familiar environments for control-design and system identification 

engineers, therefore coding of much of the flight control algorithms can be put in the hands of end-users who are 

designing the experiments. This improves turn-around time for experiments and supports a goal of the FASER 

platform, which is to make flight testing part of a preliminary evaluation of a technology such that it can be used to 

guide, rather than just confirm, the research. 

This combination of software/hardware uses widely-available COTS parts and common commercial software; 

however, the resulting system is not what many would consider turn-key. The xPC-Target support of a wide variety 

of hardware comes at the expense of a dynamic code base and a certain reliance on end-user testing. Updates from 

The Mathworks, both in scheduled releases and with interim web downloads, often break compatibility with prior 

versions, requiring at a minimum a reinstallation of the boot PROM on the PC/104 motherboard. Hardware drivers 

are also dynamic, even if the hardware is not. Standard motherboard-based serial ports now have three different 

driver routines available within xPC Target. Two of these drivers are labeled obsolete, and the third has a bug 

which causes it to fail in our application. Despite its faults, however, this arrangement provides the flexibility for 

rapidly prototyping real-time flight controllers and allows for hardware reconfiguration/upgrades. Overall this 

flexibility more than makes up for the lack of stability. 

Table 3 Primary avionic components for the FASER aircraft 

Component Manufacturer Part Number Specifications 

Computer Real-Time Dynamics CML16686GX PC/AT, 300 MHz CPU, 128 MB Memory, 

2 Serial, Real time clock, 2-USB ports, 

10/100 Base T and TX network connection 

PWM/Counter Diamond Systems Quartz-MM-10 10 Channels, independent 16-bit counters 

Analog I/O Diamond Systems MMO-32-AT A/D: 32 Ch. SE 16-bit, 200K sample rate 

D/A: 4 Ch. 12-bit; Auto-calibration 

INS/IMU Microbotics, Inc MIDG-II GPS, 3-axis accel/gyro/mags. INS solution 

Gyros: ±300 °/sec, non-linearity 0.1% of FS 

Accelerometers: ±10g, non-linearity 0.3% of 

FS

RC Transmitter/Receiver JR JR-10X/JR945S 72 MHz, 10 Channel, fully programmable 

Servos DS8411 Torque: 220 in-oz @ 4.8V, 260 in-oz @ 

6.0V 

Speed: 0.18 sec/60° @ 4.8V 

JR 

RF Modem FreeWave FGR-09 900 MHz, Spread Spectrum, 115 KBaud 

RF Camera System BlackWidow AV Flex-WAV 2.4 GHz RF, camera 480 lines color, NTSC. 

Safety Switch ElectroDynamics, 

Inc. 

SP-112 6 Channel, 2-Input 1-Output Switch 

Control surface position 

transducers 

NEI, New England 

Instruments and 

Betatronix Inc. 

50BFB502-

L815 

and 05BF8375 

Molded Conductive Plastic Element, 0-5V, 

5KΩ

Range: ±55°, Accuracy: 0.25 deg 

Air Data Birds NASA LaRC FDV series α: Range: ±100°, Accuracy: 0.35°

β: Range: ±55°, Accuracy: 0.27°

V: Range: 29.5 to 200 ft/sec, Accuracy: 0.5 

ft/sec 

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Telemetry Systems 

Radio frequency telemetry is required for all flight operations. A high-end 72 MHz RC receiver and transmitter 

pair is coupled with high-torque servos to provide full control of the vehicle. These components are off-the-shelf in 

the RC aircraft market and this widespread use makes them well understood and inexpensive. Engaging computer 

control of the vehicle is handled by a 6-channel safety-switch, triggered from the hand-held RC transmitter. For 

safety reasons, loss of signal at the RC receiver causes a failsafe mode which returns the switch to the RC inputs and 

puts the vehicle into a power-off turn. This effectively terminates the flight if the range of the hand-held RC 

receiver is exceeded, even if the vehicle is flying under computer control. 

The flight computer also has serial port access to a wireless modem. This provides 115 Kbaud rate transmission 

and reception over a 900 MHz link. This bandwidth is not sufficient to downlink all the data being recorded; 

however, it provides a means for ground crews to monitor flight condition data, such as velocity, angle of attack, and 

battery status during the flight. Parameters such as control gains, autopilot modes, maneuver size, etc., can be set in 

the flightcomputer through datalink from a ground based computer. The final RF link is a 2.4 GHz transmitter 

which sends down color video, in standard NTSC format, from a small camera mounted on the nose of the vehicle. 

Instrumentation 

Quality instrumentation is critical to making the vehicle useful as a research aircraft. The single most expensive 

component in FASER is the inertial navigation unit. At this scale, micro-gyros are appropriate; however, poor lowfrequency performance in these devices means that for accurate attitude solutions, integration with magnetometers 

and GPS receivers are critical. The unit selected for FASER was the MIDG-II from Microbotics, Inc. The device 

contains a 3-axis magnetometer, a 3-axis accelerometer, a 3-axis rate-gyro, and a GPS receiver. The MIDG-II 

provides these raw measurements and also an INS solution with error estimates, implemented through Kalman 

filtering on an internal microprocessor. The GPS solution is updated at 5 Hz, while the INS solution and raw IMU 

measurements are available at 50 Hz, with all signals transmitted over an RS422 serial stream. The MIDG-II unit is 

in a miniature package, approximately 1.5 in. square, less than 1 in. tall and weighing only 2 oz. 

All control surfaces are instrumented with potentiometers to provide control surface deflection data, and 

reference voltages are recorded to compensate for any drift. Two vanes mounted on each wingtip are instrumented 

with potentiometers to provide α and β measurements. These probes also have a small spinning propeller that, 

through a frequency counter in the flight computer, provides velocity measurements. Additional analog channels are 

used to record current and voltages from the computer and motor batteries. These give total power and a running 

total of amp-hour usage that allows for maximizing flight times. 

Currently only open-loop flight testing has been done, with the vehicle maneuvers executed by the pilot, as well 

as maneuvers executed by scheduled surface displacements implemented by the control computer. For these 

experiments, only data storage is required; however, looking towards closed-loop experiments, it is important to 

process some of the data onboard to improve quality and parse serial streams. To reduce noise, the analog channels 

are over-sampled, typically at 400 Hz, and then run through a moving average filter before down-sampling to 

storage rate of 50 Hz. Data from the INS system is available at 50 Hz, but comes in the form of a serial stream with 

packet headers, binary data, and checksums. A C-program linked in the Simulink® diagram buffers and processes 

this stream, extracting relevant data and making it available as a 50 Hz signal line within the Simulink® diagram. 

Frequency measurements from the counter boards, which read velocity probes and the motor tachometer, are prone 

to spurious spikes due to signal noise on the edge-triggered timer circuits. Using a moving averaging filter on these 

signals can extend the errors out in time as the spikes are usually well out of the range of expected values. A better 

approach is to simply remove bad points and replace them with the last good value from the sensor. A combination 

of saturation, signal-rate logic, and averaging is done to remove these spikes and provide smooth data for use in 

flight. 

A typical flight will contain the following channels of data: 

(6) Control Surface Voltages 

(4) α/β Probe Voltages 

(3) Battery and Reference Voltages 

(3) Airspeed and Motor Tachometer Counters 

(1) Controller Maneuver Marker (data tagging) 

(6) IMU Accelerations and Rates 

(6) GPS Position/Velocity 

(6) INS Position/Velocity 

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(4) INS Attitude (quaternions) 

(1) GPS Mode Information (number of satellites, dilution of precision, etc.) 

(1) INS Mode Information 

These data are stored in onboard memory (128 MB available) and downloaded from the PC/104 system through 

an Ethernet connection after the aircraft has landed. The recorded data contains a record of the entire flight; 

however, maneuver markers (set by the pilot with a switch on a spare RC channel) indicate the areas of interest and 

allow for a quick review of data in the field. 

Radio Frequency Interference (RFI) 

The PC/104 control computer consistently degrades the 72 MHz RC transmission signal. A solution to this RFI 

problem has been elusive, even though significant man-hours have been invested into solving this problem. The 

degree of degradation, however, varies with placement of antennae, electrical cabling, and enclosure design. The 

space within the vehicle is very tight, approximately 1⁄4 in. larger than the PC/104 standard, and this requires custom 

enclosures for the computer. Both copper and aluminum boxes have been tested, along with a variety of ferrite bead 

configurations, on power lines, signals lines, and ribbon cables. Component testing has found no single source to be 

responsible for all the interference, however, the power supply board alone is sufficient to cause problems. Previous 

flights have been conducted with a PC/104 power supply card that features DC to DC converters to provide the 

standard PC bipolar voltage sets (±12V, ±5V) from a single unregulated battery source. The FASER system only 

requires +5V for the computer stack itself, with other equipment (FreeWave Modem, MIDG-II INS, camera system, 

etc.) able to work from unregulated supplies over a range of voltages. Therefore, the power supply board was 

removed, and replaced with a linear voltage regulator for the flight computer and a secondary unregulated battery 

for the remaining electronics. This has shown, in preliminary testing, to reduce the RFI and leave only a slight 

degradation in RC range with the flight computer operating. 

V. Flight Data 

Data Consistency 

Measured aircraft response data are checked for 

consistency using a technique called data 

compatibility analysis or data consistency check. As 

noted above, FASER has sensors that measure 

accelerations, rates, and positions associated with the 

translational motion of the aircraft and the rotational 

motion about the c.g., as well as the magnitude and 

orientation of the air-relative velocity. Refs. 10 and 

11 provide standard methods for correcting 

measurements from each sensor location to the 

aircraft c.g. Once the measurements are corrected to 

the aircraft c.g., kinematic relationships can be used 

to check that the measurements are mutually 

consistent. 

Figure 3 Data compatibility analysis 

If all the measurements were perfect, the data 

compatibility analysis would show that the kinematic 

relationships are perfectly satisfied by the sensor 

measurements. In practice, all sensors have both 

systematic and random errors. The kinematic 

relationships are used as a tool to quantify these 

instrumentation errors and correct the measured data 

from the sensors for systematic errors. This is an 

important task, which results in a kinematically 

consistent data set with improved accuracy. 

Equations for kinematic relationships among the 

measured response variables can be found in Ref. 10. 

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This reference also discusses details of the optimization problem that is solved to calculate values for systematic 

instrumentation errors. 

Note that the discussion here only applies for measurements associated with the rigid-body motion of the 

aircraft which can be checked for compatibility with other 

measurements. Control surface deflections, power settings, and 

pilot inputs cannot be checked against others because there are 

no kinematic relationships between these measurements and 

others in the data set. 

Table 4 Systematic instrumentation error 

parameter estimates, FASER maneuver 070403 

Parameter Value 

–0.0687 ± 0.0016 ax b

Figure 3 shows measured data for airspeed, angle of attack, 

and pitch angle, with corresponding traces of the same quantities 

reconstructed from other measurements. For example, the 

reconstructed angle of attack comes from solving equations 

relating translational accelerations and body-axis angular rates to 

the angle of attack. This involves integration of kinematic 

differential equations (to go from acceleration to velocity in a 

rotating axis system) and trigonometric relationships (to go from 

body axes velocity components to angle of attack). Similar 

calculations are done to compute reconstructed airspeed and 

pitch angle. 

–0.0162 ± 0.0007 az b

 0.0004 ± 0.0001 bq

–0.1520 ± 0.0038 λV

 0.1028 ± 0.0094 λα

λθ

A simple model is introduced to characterize the errors for each sensor. The error model takes the form 

–0.0210 ± 0.0012 

z yb = (1 + ++ λ ) ν (1) 

where z indicates the measurement from the sensor, ν is the random measurement noise, and is the true value 

of the measured quantity. The scale factor error parameter 

y

λ and bias error parameter b implement a simple model 

for the systematic instrumentation error of a sensor, as depicted in Figure 4. Most sensors do not have both scale 

factor error and bias error, so that the error model for most sensors is a simpler version of Eq. (1). Data analysis 

techniques and experience with similar sensors from other flight tests are used to determine the form that the error 

model should take for each sensor. Next, the systematic instrumentation error parameters are estimated to give the 

best match, in a least squares sense, between sensor measurements and reconstructed values of the same physical 

quantities. Estimated instrumentation errors for the data shown 

in Fig. 3 are given in Table 4. Because the data shown in Fig. 3 

are from a series of elevator pulses, only longitudinal response 

was present, and only the systematic error parameters for sensors 

associated with the longitudinal response could be estimated 

from the data. The data shown in Fig. 3 have been corrected 

using the estimated instrumentation systematic error parameters 

given in Table 4. The excellent fit shown in Figure 3 and the 

accurate estimates of the systematic error parameters in Table 4 

indicate that the measurements are accurate and consistent after 

corrections are made using the systematic instrumentation error 

estimates. The estimated systematic error parameters in Table 4 

are relatively small, indicating that the sensors have small 

systematic errors. From Eq. (1), if a sensor has estimated 

systematic error parameters λ and equal to zero, 

measurements from that sensor have only random errors. 

Figure 4 Systematic error model for a sensor b

Data compatibility analysis is important because if it is not done, then any modeling based on the uncorrected 

data will have errors resulting from the fact that the modeling will incorporate effects that are really systematic 

instrumentation errors. 

INS/GPS Data 

An example of the INS/GPS data from the MIDG II unit is shown in Fig. 5. The figure shows the ground track 

from approximately one minute of flight data. The aircraft is flying a race track pattern alongside the runway at an 

approximate altitude of 200 ft. Both the 5 Hz GPS measurements, directly from the GPS receiver (red dots), and the 

50 Hz integrated navigation solution for position from the INS are shown. The navigation solution tends to lead the 

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GPS solution, compensating for time lags inherent in the GPS information. These position measurements agree 

quite well, with an average difference of about 15 ft. About mid-way through the flight the GPS measurements fail 

to update with only a single new point over a two second interval. During this time however, the navigation solution 

continues to provide a smooth position estimate, demonstrating an ability to accommodate brief outages in the GPS 

measurements.

Figure 5 Ground track, using the GPS and INS solutions from the MIDG II unit, of FASER flying a race track 

pattern. 

GPS drop outs 

VI. Summary 

The objective of the FASER project is to provide an inexpensive unmanned aerial vehicle to conduct frequent 

flight test experiments for developing and demonstrating advanced methods for experiment design, data analysis, 

dynamic modeling, and control law design. This paper describes the development of the test technique to meet this 

objective. The aircraft is outfitted with high-quality instrumentation to measure all the aircraft inputs and responses, 

in order to accurately capture the flight dynamics. Health of the vehicle energy systems are monitored. Flight data 

can be telemetered to a ground station in real time for analysis. Outrunner motors coupled to Lithium-Polymer 

batteries provided the best propulsion system. The flight computer system is based on the PC104 platform. 

Commercial-off-the-shelf hardware and software was used as often as possible. Extensive wind-tunnel testing has 

been conducted, and a six degree-of-freedom simulation with nonlinear aerodynamics has been developed to support 

flight tests. During the development of FASER, it was found that the computer systems produced significant 

72MHz radio frequency interference. This significantly delayed the development of the research platform. Also, 

significant unexpected time was spent in developing the commercial-off-the-shelf hardware and software for use 

with FASER flight test objectives. Flight tests to date have been conducted to mature the flight operations, validate 

the instrumentation, and check the flight data for kinematic consistency. Data compatibility analysis showed that 

American Institute of Aeronautics and Astronautics 

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the flight data are accurate and consistent after corrections are made for estimated systematic instrumentation errors. 

With the RFI reduced to acceptable levels, FASER is ready for flight tests to develop and demonstrate advanced 

dynamic modeling and control law design methods. 

Acknowledgments 

The authors gratefully acknowledge financial and logistical support for the FASER project from the NASA 

Aviation Safety Program, led by Dr. Christine Belcastro at NASA LaRC. The authors thank Butch Watkins and Jeff 

Hill of NASA LaRC for support in flight operations. Thanks also to Gene Adams, Steve Riddick, and Vanessa 

Aubuchon at NASA LaRC for research support, and to Dan Murri, test pilot for the FASER project. 

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