May 2011 

NASA/TM–2011-217145 

A Data System for a Rapid Evaluation Class of 

Subscale Aerial Vehicle 

Edward F. Hogge 

Lockheed Martin, Hampton, Virginia 

Cuong C. Quach and Sixto L. Vazquez 

Langley Research Center, Hampton, Virginia 

Boyd L. Hill 

Vigyan Inc., Hampton, Virginia 
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May 2011 

NASA/TM–2011-217145 

A Data System for a Rapid Evaluation Class of 

Subscale Aerial Vehicle 

Edward F. Hogge 

Lockheed Martin, Hampton, Virginia 

 Cuong C. Quach and Sixto L. Vazquez 

Langley Research Center, Hampton, Virginia 

Boyd L. Hill 

Vigyan Inc., Hampton, Virginia 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

The use of trademarks or names of manufacturers in this report is for accurate reporting and does not 

constitute an official endorsement, either expressed or implied, of such products or manufacturers by the 

National Aeronautics and Space Administration. 
1 

Abstract

A low cost, rapid evaluation, test aircraft 

is used to develop and test airframe damage 

diagnosis algorithms at Langley Research 

Center as part of NASA's Aviation Safety 

Program. The remotely operated subscale 

aircraft is instrumented with sensors to 

monitor structural response during flight. 

Data is collected for good and compromised 

airframe configurations to develop data 

driven models for diagnosing airframe state. 

This paper describes the data acquisition 

system (DAS) of the rapid evaluation test 

aircraft. A PC/104 form factor DAS was 

developed to allow use of Matlab®, 

Simulink® simulation code in Langley's 

existing subscale aircraft flight test 

infrastructure. The small scale of the test 

aircraft permitted laboratory testing of the 

actual flight article under controlled 

conditions. The low cost and modularity of 

the DAS permitted adaptation to various 

flight experiment requirements. 

Nomenclature

= angle of attack, deg. 

= angle of sideslip, deg. 

 = aileron deflection, deg. 

 = elevator deflection, deg. 

 = rudder deflection, deg. 

 = Euler yaw angle, deg. 

 = Euler pitch angle, deg. 

 = Euler roll angle, deg. 

ADC = Analog to Digital Converter 

AHRS = Attitude and Heading Reference 

Sensor 

AirSTAR = Airborne Subscale Transport 

Aircraft Research 

BIOS = Basic Input Output System 

c.g. = Center of Gravity 

COTS = Commercial off-the-shelf 

CPU = Central Processing Unit 

DAS = Data Acquisition System 

DOS = Disk Operating System 

EMC = Electromagnetic Compatibility 

EMI = Electromagnetic Interference 

FASER = Free-flying Aircraft for Subscale 

Experimental Research 

GPS = Global Positioning System 

IMU = Inertial Measurement Unit 

INS = Inertial Navigation System 

I/O = Input/output 

ISA = Industry Standard Architecture (IBM 

PC bus standard) 

PC-AT = IBM PC with “advanced 

technology” bus standard

PSD = Power Spectral Density 

R/C = Remote Control 

RAM = Random Access Memory 

RPM = Revolutions per minute 

UAS = Unmanned aerial system 

Introduction 

The use of a low-cost subscale unmanned 

aerial system (UAS) enables high risk 

research experiments to be flown under 

realistic conditions unavailable in a 

laboratory or computer simulation setting. 

The low cost and lower risk as compared to 

human piloted flight testing allow a wider 

range of flight experiments to be considered. 

The SIG Edge 540T subscale test aircraft is 

built from a kit by SIG Manufacturing 

Company, Inc. It is an inexpensive and 

spacious platform for the necessary sensors 

and computer processing electronics (Figure 

1). The test aircraft is a 1:33% subscale 

version of the Zivko Aeronautics Inc. Edge 

540 T tandem seat aerobatic aircraft. 

This report describes the instrumentation 

used for the Edge 540T test aircraft research. 

The experiments flown using this platform 

include 1) development of an algorithm to 

 
2 

 diagnose damage to the wing spar structure 

using actual damage, 2) development of an 

algorithm and hardware to predict time to 

depletion of the propulsion batteries, 3) a 

software health experiment testing runtime 

techniques for monitoring avionics software. 

A high-level language simulation 

environment was chosen for the airborne data 

acquisition system (DAS). The 

MathWorks® Matlab, Simulink, and RealTime Workshop® real-time operating system 

provides a familiar development environment 

for mechanical, aeronautical, and electronics 

engineers. The low cost remotely piloted 

vehicle with a research computer compatible 

with the MathWorks tool chain built on 

earlier work demonstrated by the Langley 

Research Center Free-Flying Aircraft for 

Sub-scale Experimental Research (FASER) 

[1]. See Figure 2. 

To the extent possible, the FASER 

architecture [2] was adopted so that available 

resources could be devoted to studying the 

airframe diagnosis and other experiments 

without having to go through a hardware and 

tool selection process redundantly. Existing 

I/O device drivers reduce the development 

effort, and standard data file formats enable 

easier collaboration across research 

organizations. Some file formats used 

include Matlab matfile (.mat) for engineering 

parameters and Microsoft Excel Workbook 

(.xls) for calibration and signal lists. The 

Airborne Subscale Transport Aircraft 

Research (AirSTAR) test bed project 

provided a Simulink software block diagram 

library of data acquisition software modules 

and aerodynamic calculation modules. These 

were used for the airborne DAS and as a 

starting point for a rudimentary simulation. 

Laboratory motion experiments were done 

with a hardware-in-the loop version of this 

simulation to drive a motion base. An open 

source file configuration management 

process used in other AirSTAR experiments 

was used as well. Some changes were made 

to take advantage of the FASER experience, 

but on an airframe more suitable for 

 

Figure 1: View of the Edge 540T R2 Test Aircraft (Photo Courtesy 

of Tom Vranas) [11] 
3 

structural testing. The limited size of the 

FASER Hangar 9 UltraStick 120 test aircraft 

fuselage [2], width 4.7 in., relative to the 

DAS flight stack, 3.5 in., made wiring 

changes difficult. 

The larger Edge 540T R2 subscale test 

aircraft facilitated the integration of the 

electronics package and permitted flexibility 

in sensor wiring. The larger fuselage width, 

12.5 in., and the larger payload capacity from 

the larger weight, 41 lbs., increased the 

number of feasible experiments. For 

comparison, the fuselage width of the 

FASER was 4.7 in. and the weight was 16.5 

lbs. Spare payload capacity of the Edge 

accommodated multiple experimental 

payloads per flight. The use of a sizeable 

removable cockpit facilitated the alignment 

and software integration of the attitude and 

GPS sensors as the sensors could be treated 

as a self-contained subsystem. Accessibility 

was improved and compass alignment and 

other tests performed without the large 

airframe being involved. This configuration 

permitted work on the avionics, airframe, and 

power plant to proceed in parallel. 

Other Edge UAS features included a COTS 

data system to measure selected vehicle 

parameters. A ground tracking camera, an 

on-board wing camera, and a research 

maneuver event marker were used to identify 

research maneuvers and check the logged 

attitude data. 

Edge 540 Data Acquisition System 

The on-board data acquisition system (DAS) 

for the Edge 540 Subscale UAS was designed 

to acquire and log engineering parameters 

during a 10-15 minute flight operation. The 

data system was also designed to operate 

with laboratory experiments of wing spar 

damage using a motion base to simulate 

flight maneuver loads. The DAS (Figure 3) 

consisted of a single board computer, boards 

to measure vehicle analog sensors, boards to 

condition signals, and a board to regulate 

power and a board for serial communications. 

 

Figure 2: View of the FASER Ultra Stick 120 Test Aircraft [2] 
4 

The DAS computer is an Ampro Core 

Module 620 Single Board Computer. It has 

an AMD GeodeTM LX 800 processor and 

complies with the PC/104 form factor 

standard (Figure 4). The single board 

computer has the PC-AT architecture to 

execute the code generated from a Simulink 

model using the xPC Target® real-time 

operating system [3]. This environment 

corresponds to a similar laboratory hardware 

in-the-loop Matlab/Simulink simulation 

environment. This is a platform familiar to 

developers and compatible with the AirSTAR 

software architecture [4]. It allows use of 

high-level block diagram programming tools 

and also fits within the weight and vibration 

constraints of the test aircraft environment. 

The CPU speed is moderate by today’s 

desktop machine standards as indicated by 

the comparison in Figure 5. However, it uses 

low power and does not require a cooling fan. 

It has 512 MB of plug-in RAM. A compact 

flash disk drive, 8 GB, stores the logged data. 

File size is constrained by the 32-bit 

addressing of the file conversion program 

provided. This permits a 25-minute 

recording of logged channels at a 500Hz 

sampling rate. 

The DAS block diagram (Figure 6) shows 

various communications and measurement 

interfaces for components listed in Table 1. 

Two 32-channel analog input and output 

(I/O) modules, 4-channels of serial 

communication RS-232 and an Ethernet I/O 

module are present. An attitude and heading 

reference sensor (AHRS), a Global Position 

System (GPS) sensor and an optional user 

payload are attached through serial RS-232 

ports. Voltage level shifters for the RS-232 

channels allow use of “hot swapping” of the 

Figure 4: AMD Geode 500 MHz CPU and 

Ethernet Daughter Board 

Figure 3: Edge 540 R2 DAS Flight Stack on 

the Bench 
5 

canopy serial lines according to the RS-232 

standard. Figure 6 depicts the data bus and 

major system cable connections. The CPU 

board is at the bottom of the stack and 

provides the best time source for 

synchronizing parameter measurements. The 

500 Hz sampled analog boards (blocks 

analog I/O 1 and 2) have the tightest time 

synchronization. The AHRS and GPS ASCII 

code stream are logged asynchronously at 20 

Hz and 1 Hz respectively. 

A user payload can be optionally logged at 10 

Hz. A 2 Hz sine wave analog signal is 

produced by analog output and serves as a 

simple time reference for other on-board data 

systems. It also serves as a system activity 

monitor when added to the downlink 

telemetry stream and provides situational 

awareness of DAS system health during 

flight operations. 

 

Figure 5: AMD Geode LX800 CPU on DAS Main Board Comparison 

with Typical Desktop PC Computer Processors [10] 
6 

 

Figure 6: Block Diagram of the Edge 540T R2 DAS 
7 

Table 1: Primary DAS Components 

Component Manufacturer Model or 

Part Number 

Specifications 

Computer AMPRO 

Computers Inc. 

CoreModule 620 

AMD Geode 

PC/AT, 500MHz CPU, 512 MB 

Memory, 2 Serial, 2-USB ports, 

10/100 Base T and TX Ethernet 

Compact Flash Kingston 

Technology 

CF/8GB IDE 3.3V, 8GB 

Analog I/O Diamond Systems DMM-32DX-AT A/D: 32 Ch. SE, 16 bit, 250K 

sample rate, auto calibration 

D/A: 4 Ch. 12 bit 

DC/DC Power 

Supply 

Diamond Systems JMM-512-V512 50W, +5V, 10A; +12V, 2A -5V, -

12V outputs 

Serial I/O Diamond Systems Emerald-MM 4-Channel RS-232 

Serial I/O level 

shifter 

Acroname, Inc. R115-INT-BRD-1 +-12V conversion to TTL 

Telemetry 

transmitter 

RCAT Systems RCATSTM-UAV 900MHz Telemetry 

10 Hz sampling 

Attitude 

Heading 

Reference 

System 

(AHRS) 

VectorNav VN-100 3-axis accel./gyro/magnetometer 

INS solution 

Accelerometers: +-6g, nonlinearity 0.5% of FS 

Gyros: +-500 deg/sec, non-linearity 

1% of FS 

GPS 

antenna/sensor 

USGlobalSat, Inc. EM-406A 1 Hz NMEA output command 

Air Data Vanes RCAT Systems Custom built +-1 deg accuracy, 0.75 deg 

resolution, carbon fiber 

Barometric 

Static Pressure 

Freescale 

Semiconductor, 

Inc. 

MPXAZ6115AP 45.9 mV/kPa, +-1.5% FS 

Barometric 

Differential 

Pressure 

Freescale 

Semiconductor, 

Inc. 

MPXV7002DP 1000 mV/kPa +-2.5% FS 

Wing 

Accelerometers 

Analog Devices EVALADXL335Z Accelerometers: +-3.6g, nonlinearity .3% of FS 

Wing Strain 

Gages 

Measurements 

Group, Inc. 

CEA-060187UW350 Constantan, Gage Factor 2.085 +-

0.2% 

Wing Spar 

Strain Gages 

Measurements 

Group, Inc. 

WK-13-125AD350 K-alloy, Gage Factor 2.09 +- 1.0% 

 
8 

Figure 7 is the Simulink diagram for the 

normal configuration of the DAS. The two 

blocks in the upper left are the analog input 

card device drivers. The lower half of the 

diagram show the blocks associated with the 

RS-232 serial I/O streams. Two VectorNav 

VN-100 attitude devices, a GPS device, and 

an optional user payload serial device emit a 

serial character stream which is logged to the 

PC/104 single board computer flash memory. 

This diagram is converted to C-code by the 

Real-Time Workshop code generator and 

Figure 7: Simulink Diagram of the DAS, Normal Software Configuration 
9 

downloaded to the real-time operating system 

xPC Target. 

RCATS telemetry system 

A COTS data system, RCATS, Figure 8, is 

present to provide altitude and airspeed 

information to the remote pilot. It has a low 

speed transmitter to downlink selected 

parameters at 10 Hz during flight. It 

functions during the development of the 

Research DAS to give a reliable air speed and 

altitude ground read out to assist the pilot. 

Once confidence is gained in the Research 

DAS it can be removed. The Research DAS 

and the RCATS each logged engineering 

parameters using on-board compact flash 

memory. 

Table 2: RCATS Sensors and Sampling Rate 

Sensors Locations Analog

Signals

Rate

Structure Characterization 

CG acceleration 1 position (z) 1 10 Hz 

Aerodynamic Characterization 

Airspeed Pitot RCATS Total and Static port 1 10 Hz 

Altitude RCATS Pitot Static port 1 10 Hz 

Propeller RPM Spinner RCATSTM 

(Hall effect sensor) 1 10 Hz 

Flight Management 

Position GPS RCATS (Lat, Lon, Alt) 3 1 Hz 

Position GPS USGlobalSat 3 1 Hz 

Motor temperature motor housing 1 10 Hz 

Figure 8: RCATS System for Altitude and 

Airspeed for the Remote Control 

Pilot 
10 

Table 3: Research DAS Sensors and Sampling Rate 

Sensors Locations Analog

Signals

Rate

Structure Characterization 

Strain 9 positions span wise 9 500 Hz 

Wing acceleration 6 positions (x, y, z) 18 500 Hz 

CG acceleration 2 positions (x, y, z) 6 500 Hz 

Motor Mount 

acceleration 

1 position (x, y, z) 

3 500 Hz 

Event marker N. A. 1 500 Hz 

Aerodynamic Characterization 

Attitude Heading 

Reference Sensor 

1 position (yaw, pitch, roll) 

3 20 Hz 

Attitude Heading 

Reference Sensor 

1 position attitude(quaternion) 

magnetic vector 

acceleration vector 

body rotation rates 

4 

3 

3 

3 

20 Hz 

Airspeed Pitot Total and Static port 1 500 Hz 

Altitude Pitot Static port 1 500 Hz 

Ambient 

Temperature 

1 position 

1 500 Hz 

Propeller RPM Spinner (Hall effect sensor) 1 500 Hz 

Ailerons 2 positions 2 500 Hz 

Elevators 2 positions 2 500 Hz 

Flaps 2 positions 2 500 Hz 

Rudder 1 position 1 500 Hz 

Flight Management 

Motor Current motor speed controller 2 500 Hz 

Motor battery 

temperature 

motor batteries 4 500 Hz 

Motor battery 

currents 

motor batteries 4 500 Hz 

Motor battery 

voltages 

motor batteries 4 500 Hz 

 
11 

The logged measurements for the DAS are 

harvested by Ethernet download at the end of 

the flight while the logged measurements for 

the RCATS are harvested by the removal of 

flash media. The parameters logged are 

listed in Table 2 and Table 3. 

Signals Logged by the DAS 

Table 3 is a list of location and sampling 

rates for the research data sensors. Structure 

characterization instrumentation consists of 

wing structure strain gages and wing 

accelerometers. 

Aerodynamic model characterization 

instrumentation consists of total and static 

probe barometric sensors for altitude and 

airspeed. The probe is also fitted with vanes 

to measure angle-of-attack () and angle-ofsideslip (). The propeller revolution speed 

sensor gives a thrust measurement, an inertial 

navigation position and orientation sensor, 

and a 3-axis roll rate sensor give kinematic 

motion parameters yaw (), pitch (), and 

roll (). The aileron (), elevator (), 

rudder (), and flap deflection signals are 

necessary for control input configuration for 

the aerodynamic model identification. 

Other sensors record parameters related to 

vehicle health such as battery voltage, battery 

current, battery temperature, and propulsion 

motor current. The DAS logs these sensors, 

and selected ones are plotted for a typical 

flight in Figure 9. The static pressure, which 

is inversely related to altitude, defines the 

extent of the flight from takeoff to landing. It 

is seen in the fourth strip chart of Figure 9. 

The first strip chart depicts the current to one 

of the propulsion motors and reaches a 

maximum of 100 Amperes during the takeoff 

climb to altitude. The event marker trace is 

high during two straight track cruise 

maneuvers to take data for a structural failure 

diagnosis experiment completed at 360 

seconds. 

The two periods of zero current that follow 

are two stall maneuvers with two flap settings 

as shown in the parallel third strip chart, 

green trace. Even though the throttle was 

completely off and no current was 

commanded to the propulsion motors, the 

propeller revolutions per minute (RPM) did 

not go to zero because the motors are 

programmed to free wheel when off, 

allowing the propeller to be driven by the 

apparent wind produced by the forward 

motion. After the landing at approximately 

520 seconds, the activity in the strip charts 

between 620 to 680 seconds is due to a taxi 

along the runway back to the flight line. 

 
12 

 

Figure 9: Selected Parameters from a Typical Flight 
13 

Attitude and GPS Sensor 

A range of INS options are available for this 

size vehicle. Since the structural damage 

diagnosis research did not have a stringent 

accuracy requirement on the estimate of 

attitude and rotation rates, a lower cost 

system was selected. The lower cost system 

did not integrate the GPS measurements with 

the attitude inertial sensors but rather relies 

on the local magnetic and acceleration 

measurements to obtain an attitude solution. 

A VectorNav VN-100 attitude heading 

reference sensor (AHRS) was used. This was 

located near an USGlobalSat GPS sensor 

under the canopy, Figure 10. The attitude 

sensor’s default state worked acceptably in 

pitch and roll, but yaw exhibited spurious 

mirror image rotation reversals and 

nonlinearity dependent upon compass 

quadrant heading. This effect was in the 

recorded data and is not easily corrected. 

The factory default for the assumed 

orientation of the Earth magnetic vector was 

that of the Western U.S. The reference Earth 

magnetic vector was corrected to the 

orientation of the Eastern U.S. 

To reduce vibration effects, the 

accelerometer’s contribution was 

deemphasized and the magnetometer’s and 

rate gyros’ contributions emphasized in the 

Kalman INS solution. This solved the 

problem of rotation reversals during taxi and 

ground operations. However, the problem 

persisted during flight and is believed to be 

propeller vibration-related. A possible 

mitigation strategy being considered is to 

soft-mount the sensor. 

A nonlinearity problem in heading (yaw) was 

addressed by sampling of all orientations to 

make corrections of hard and soft iron effects 

(Figure 11). A non-ferrous level table 

oriented North/South was used to establish 

attitude correction factors with the avionics 

operating. It had been anticipated that the 

strong magnets in the electric propulsion 

motors would bias the Earth reference 

magnetic field, but this effect was negligible 

at the installation location of 26 inches 

Figure 10: VN-100 Attitude Sensor and 

USGlobalSat GPS Sensor 

Shown with Compass used 

During Alignment. 

Figure 11: Laboratory Use of Non-magnetic 

Level Table for Hard/Soft Iron Compensation 

of AHRS Magnetic Reference Vector 
14 

between the motor and the attitude sensor. 

EMC Testing 

As a matter of precaution, EMC testing is 

performed on various aspects of the 

instrumentation and control electronics [5], 

[6]. Procedures call for a radio range test to 

be performed before each flight deployment. 

This is described in [5]. An emissions study 

was performed on the installed 

instrumentation in a reverberation chamber. 

This is described in [6]. 

This section describes an early evaluation of 

potential interference on strain gauge data 

channels from sources associated with the 

Edge airplane. Of particular concern are the 

transmitters for the remote control (R/C), the 

RCATS, and the XBEETM alternative 

downlink. (The XBEE transmitter was used 

 to configure the attitude inertial sensor 

before flight and turned off during flight 

because of this interference. It was removed 

during later flights because the attitude 

system could be configured in the laboratory 

more effectively.) 

For this evaluation the DAS was started in 

record mode. Then the three systems were 

started up and shut down and the control 

surfaces moved using the servos. Figure 14 

plots the EMI effect on the strain gauge S4 

located in left outboard position of the wing 

tube as diagrammed in Figure 12 and 

Figure 13. 

Figure 12: Strain Gage Locations 
15 

The Edge is piloted using a COTS JR-12XTM 

control radio. It is a robust remote control 

(R/C) system which reduces the probability 

of self-interference by executing a frequency 

selection process during the transmitter and 

receiver start up as described in [5]. The 

sophistication of the JR-12X enables it to 

avoid congested frequencies in the 2.4GHz 

band but appears to impart some measurable 

signals on the S4 during the selection 

process. As seen in Figure 14, the selection 

process initially created spikes on the S4 

strain signal. The initial spikes became quiet 

once the negotiation was complete. 

A test motion of a control surface by a servo 

caused loading of strain gage S4 through 

excitation of a natural resonant vibration 

mode that moved the wing. This is the spike 

following the “command servo motion” 

annotation. 

Selection of a frequency away from selfinterference sources such as the PC104 CPU 

clock (500 MHz) made the control system 

more robust than the technology available 

during FASER development [2]. Diversity of 

receiver locations also may have helped. A 

proof test was done on the ground as a buildup to flight and as a part of the laboratory 

verification of control functionality before 

flight. Two telemetry data links were also 

tested for self-interference. The XBEE 

telemetry data link transmitter imparted 

enough power to the twisted pair strain gage 

wiring that the low-voltage strain signals 

were overwhelmed. Unshielded twisted-pair 

strain gage wiring was used to simplify cable 

fabrication and reduce weight. 

Better EMI rejection may result from 

shielding and enclosure of the data logging 

computer and power system, at the cost of 

weight, space, and flight duration, so was not 

implemented for these flights. This is 

discussed in [6]. 

The RCATS telemetry transmitter exhibited a 

much smaller effect on the low-voltage strain 

signals, below a significant fraction of the 

signal level. It was used in flight to relay 

altitude and airspeed. The annotations in 

Figure 14 describe when each subsystem was 

turned on and off during a self-interference 

test. 

 

Figure 13: Wing Spar Detail of Strain Gage Locations 
16 

Live Propeller Vibration testing 

A live propeller was operated with the test 

aircraft secured with a steel cable to the wall 

of the NASA Langley Research Center High 

Intensity Research Facility Reverberation 

Chamber. The aircraft was powered up in 

flying configuration with battery power to the 

motor and data logging electronics. The 

aircraft was controlled through a rebroadcast 

of a control signal from the control room to 

the test cell. The research test pilot 

commanded the motor to maintain 4000 RPM 

(67 Hz) and the aircraft sensors were logged 

by the Edge540 DAS. The accelerometers 

were located as shown in Figure 15. One run 

was performed with the acceleration sensor 

powered off to record any EMI effects on the 

passive channels. Other runs were done with 

the acceleration sensors powered on as during 

normal operations. This way, the 

measurement effects due to vibration could 

be separated from those due to EMI. 

 

Figure 14: Self-Interference Effects on Left Wing Spar Strain Gage Signal 
17 

Figure 16 shows a combined plot of the 

power spectral density (PSD) of the vertical 

axis (Z) component of the accelerometer at 

the nominal center of gravity (c.g.). The blue 

trace is with the accelerometer preamplifier 

powered on. The red trace is with the 

accelerometer preamplifier powered off. A 

low-pass 50-Hz filter was present at the 

sensor to prevent aliasing. This test was 

performed at a high sampling rate (2200 Hz) 

compared with a 200 - 500 Hz nominal 

sampling rate for research flights. A 

Hamming window [7] using Welch’s method 

was used to process the data to generate the 

PSD [8]. The spikes on the blue trace were 

due to the propeller frequency and its 

harmonics. No evidence of the propeller 

spikes can be seen on the red trace. The 

periodic ripple is an artifact of the windowing 

method. This indicates that there was no 

significant EMI problem with the 

acceleration data path because the signal’s 

envelop decays below 40dB in the region of 

interest. 

 

Figure 15: Accelerometer Locations 
18 

There was an additional concern that the first 

and second harmonics of the propeller 

frequency were aliasing into the data as a 

lower frequency when the DAS was sampling 

at 200 Hz. Based on this test the sampling 

frequency was increased to 500 Hz to reduce 

any aliasing effects during the subsequent 

damage detection flights. 

Another view of the vibration test is given by 

a time domain plot of the three tube (wing 

spar) strain gages, followed by various 

accelerometers, Figure 17. The 

accelerometer plot starts with the one at the 

test aircraft c.g. followed by the one at the 

tube center. The others are progressively 

further along the left wing. This is a onetenth second snapshot to give the flavor of 

the synchronized response to the propeller 

vibration forcing input. Since this was a 

simpler dynamical configuration compared 

with free flight it may provide insight to help 

analyze the flight data. The reaction force of 

the aircraft wheels against the support surface 

may cause stronger acceleration readings 

than the case of free-flight. 

 

Figure 16: Power Spectrum of the C.G. Accelerometer with the Sensor Power On and Off 
19 

Issues

Some integration problems encountered were 

propeller vibration-related. The anti-aliasing 

filtering of the acceleration measurements did 

not suppress the propeller frequency 

adequately for the initial 200 Hz sampling 

rate. This was revealed by ground facility 

EMI testing and a flight flown with selected 

DAS channels sampled at a much higher rate 

to identify the actual noise spectra. The 

sampling rate was increased to 500 Hz and 

the number of data channels recorded 

reduced to stay within processor limitations. 

An attempt to employ a low-cost attitude 

sensor was made, but the yaw axis 

measurement was compromised by similar 

vibration effects. 

 

Figure 17: Vibration Time History at Stations from the Aircraft c.g. to the Middle of the 

Left Wing 
20 

Conclusion

The use of commercial off the shelf hardware 

with a large subscale model facilitated rapid 

development. The adaptable software 

environment facilitates changes and 

adaptation of payload experiments. Testing 

of high risk experimental algorithms and 

avionics packages is possible with minimum 

project risk due to low cost. Vibration and 

EMI problems give experimenters the realism 

necessary to see their prototype devices and 

ideas behave in a flight environment with 

minimal risk as compared with a full scale 

flight. Vibration and EMI from telemetry 

transmitters present challenges for data 

quality, but AirSTAR standard control radio 

verification procedures assure flight safety. 

 
21 

Appendix

Sensor Calibration, Acceleration 

The accelerometer channels were calibrated 

through use of the data system and a level 

table. The ADXL335 3-axis accelerometer 

evaluation board was attached to a holder that 

allowed it to be placed on the table and 

oriented parallel and anti-parallel to the local 

gravitational acceleration vector according to 

each of the three axes. The resulting 

measurements were fitted to a linear equation 

through use of the Matlab function “polyfit”. 

The calibration covered 1/3 of the ±3g full 

scale range of the sensor. This was accepted 

as part of the low-cost philosophy. A plot of 

the acceleration as a function of the sensor 

output voltage for a typical sensor is given in 

Figure 18. Table 4 summarizes the linear 

equation gain and bias factors for the other 

accelerometers. 

Table 4: Accelerometer Calibration Factors 

Sensor Gain Bias 

A1 X-axis -3.33 4.76 

A1 Y-axis -3.34 4.92 

A1 Z-axis -3.35 5.08 

A2 X-axis -3.38 5.05 

A2 Y-axis -3.33 5.01 

A2 Z-axis -3.56 5.48 

A3 X-axis -3.38 5.00 

A3 Y-axis -3.37 5.05 

A3 Z-axis -3.38 5.08 

A4 X-axis -3.34 4.99 

A4 Y-axis -3.37 5.01 

A4 Z-axis -3.34 5.00 

A5 X-axis -3.36 5.03 

A5 Y-axis -3.36 4.98 

A5 Z-axis -3.36 5.03 

A10 X-axis -3.29 4.91 

A10 Y-axis -3.23 4.87 

A10 Z-axis -3.41 5.13 

A11 X-axis -3.37 4.97 

A11 Y-axis -3.33 4.99 

A11 Z-axis -3.38 5.11 

A12 X-axis -3.37 4.98 

A12 Y-axis -3.32 5.03 

A12 Z-axis -3.37 4.97 
22 

Figure 18: A 3-point Calibration Profile for a Typical Acceleration Sensor with Standard Error 

Bars and Measured Calibration Scattered Points. 
23 

Sensor Calibration, Control Surface 

Deflection 

The control servos were modified by the 

attachment of pick-off leads on the 

potentiometer that is attached to the rotating 

actuator shaft. The DAS was operated as the 

surface was moved to zero degrees, the 

trailing edge up (TEU) deflection limit, and 

the trailing edge down (TED) deflection 

limit. The actual angle was measured using 

an alignment device [5]. The measurements 

were fitted to a linear equation similar to that 

of the accelerometers. A plot is given in 

Figure 19. Table 5 lists the linear equation fit 

gain and bias factors for the other control 

surfaces. 

Table 5: Control Surface Potentiometer 

Calibration Factors 

Sensor Gain Bias 

AILL 7.93 -44.33 

FLAPL 8.68 -47.39 

ELR -10.87 61.47 

ELL 10.59 -55.87 

FLAPR -8.03 42.93 

AILR -7.62 41.83 

RUD -16.44 85.73 

 

Figure 19: A 3-point Calibration Profile for a Typical Control Surface 

with Standard Error Bars and Measured Calibration Scattered 
24 

Sensor Calibration, Strain Gage 

Nine strain gages are installed on the Edge to 

measure strain on the load path at various 

points as illustrated in Figure 12. Three 

gages are bonded to the spar cap on the top 

surface of each wing. The remaining three 

gages are installed on the aluminum load 

carry-thru tube on the interior surface. All 

strain gages are quarter-bridge foils, each 

wired to a signal conditioning circuit shown 

in Figure 20. The output voltages of these 

circuits are connected to the analog input 

channels on the DAS. A simple calibration 

technique was devised to obtain a voltage to 

strain conversion for the strain gage channels. 

For this, potentiometers were connected to 

the signal conditioning circuits in place of the 

strain gages. A series of DAS voltage 

measurements were recorded for 

potentiometer values ranging from 348 to 

352 . The strains for the associated 

resistances are computed from the gage factor 

equation. [9] 

 	

G

G

R

R R GF 
  


Solving for Strain: 

 	

G

G

GF R

R R




 
 

where: RG is the 350 nominal gauge 

resistance 

R is the actual gauge resistance 

under test conditions 

GF is the published gauge factor 


 is the strain 

The calibration data (Table 6 and Table 7) 

includes the resistance values and the 

calculated strain and the voltages measured at 

the DAS. A second order polynomial fit is 

applied to the data in Table 6 and Table 7 to 

obtain a voltage to strain curve for the entire 

range. The results are given in Figure 21 for 

the DAS used in the aircraft. The polynomial 

fit coefficients are then used as conversion 

factors for converting voltage to strain for the 

strain channels on the DAS. 

 

Figure 20: Signal conditioning circuit for each strain gauge position. 
25 

 

Table 6: Calibration measurements for wing strain gages 

Resistance

(	

Ch1 

 (V) 

Ch2 

 (V) 

Ch3 

 (V) 

Ch7 

(V)

Ch8 

(V)

Ch9 

(V) 


348.0 -4.1800 -0.0691 -3.2648 -7.6344 -2.0230 -5.3287 -2740 

348.5 -3.4906 0.9324 -2.1632 -6.9145 -1.0283 -4.2249 -2055 

349.0 -2.1410 2.1332 -1.2493 -5.6010 0.1397 -3.3397 -1370 

349.5 -1.0557 3.1536 -0.0624 -4.5462 1.2217 -2.1403 -685 

350.0 0.1262 4.2340 1.1513 -3.3638 2.2632 -0.9219 0 

350.5 1.2771 5.0424 1.9840 -2.2387 3.1017 -0.0857 685 

351.0 2.2975 6.4240 3.0939 -1.2000 4.4518 0.9627 1370 

351.5 3.3824 7.5129 4.1772 -0.1311 5.5234 2.0942 2055 

352.0 4.1265 8.4926 5.3469 0.6057 6.5468 3.3013 2740 

Table 7: Calibration measurements for tube strain gages 

Resistance

(	

Ch4

(V) Ch5 (V) 

Ch6

(V) 


348.0 -4.5557 -0.3043 0.3132 -2734 

348.5 -3.8375 0.6926 1.4211 -2051 

349.0 -2.4150 1.8746 2.2990 -1367 

349.5 -1.3779 2.9468 3.4887 -684 

350.0 -0.2147 4.0073 4.7078 0 

350.5 0.9449 4.8427 5.5418 684 

351.0 1.9956 6.1904 6.6247 1367 

351.5 3.0993 7.3045 7.7479 2051 

352.0 3.8120 8.3017 8.9306 2734 

 
26 

 

Figure 21: Voltage to Strain Calibration 
27 

References 

1. Garza, F. and and Morelli, E. A Collection of Nonlinear Aircraft Simulations in Matlab . s.l. : 

NASA, 2003. pp. 45-49. NASA/TM-2003-212145. 

2. Owens, D., Cox, D. and Morelli, E. OwensDevelopment of a Low-Cost Sub-Scale Aircraft for 

Flight Research: The FASER Project. s.l. : AIAA, 2006. 

3. MathWorks, Inc. xPC Target Selecting Hardware Guide, February 2007 Online only. [Online] 

2007. pp. 2-14 - 2-16. 

4. Murch, A., Cox, D. and Cunningham, K. Software Considerations for Subscale Flight Testing 

of Experimental Control Laws, AIAA paper 2009-2054. 2009 : AIAA. 

5. Strom, T. Edge 540T Unmanned Aerial Research Vehicle Model Development and Testing. 

s.l. : NASA, 2011. 

6. Ely, J., et al. Radiated Emissions From Remote-Controlled Airplane, Measured in a 

Reverberation Chamber. s.l. : NASA, 2011. 000000. 

7. Oppenheim, A., and Schafer, R. Discrete-Time Signal Processing, Prentice-Hall, Inc., 1989. 

pp. 447-450. 

8. Fassios, Spilos F., and Sakellariou, John S. Fassios,Time-Series Methods for Fault Detection 

and Identification in Vibrating Structures, Phil. Trans. R. Soc. , 2007. pp. 365, 421. 

9. Wolf, Stanley. Guide To Electronic Measurements and Laboratory Practice. Englewood 

Cliffs, NJ : Prentice-Hall, Inc., 1973. p. 420. 0-13-369587-5. 

10. Mathworks, Inc. Xpc Target 4 User's Guide, September 2010 online only revised for version 

4.4 release 2010b. [Online] 2010. p.15-28. 

11. Vranas, T. Photo on p. 2 used courtesy of Tom Vranas of Vranasphoto.com. 
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