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PRELIMINARY RESULTS FROM THE 

GPS-REFLECTIONS MEDITERRANEAN BALLOON EXPERIMENT fGPSR MEBEX) * 

J. L. Garrison 

NASA Goddard Space Flight Center 

Greenbelt, MD, 20771 

G. Ruffini, A. Rius, E. Cardellach 

Institut d'Estudis Espacials de Catalunya (lEEC), CSIC Research Unit, Gran Capita 2-4, 08034 

Barcelona, Spain 

D. Masters, M. Armatys 

Colorado Center for Astrodynamics Research 

University of Colorado, Boulder, CO, 80309 

V. Zavorotny 

Cooperative Institute for Research in Environmental Sciences (CIRES) 

University of Colorado, Boulder, CO, 80309 



•Presented at the Sixth International Conference on Remote Sensing for Marine and Coastal Environments, Charleston, 
South Carolina, 1-3 May 2000. 



ABSTRACT 
An experiment to collect bistatically scattered GPS signals from a balloon at 37 km 

altitude has been conducted. This experiment represented the highest altitude to date 

that such signals were successfully recorded. The flight took place in August 1999 over 

the Mediterranean sea, between a launch in Sicily and recovery near Nerpio, a town in 

the Sierra de Segura, Albacete province of Huelva, Spain. Results from this experiment 

are presented, showing the waveform shape as compared to theoretical calculations. 

These results will be used to validate analytical models which form the basis of wind 

vector retrieval algorithms. These algorithms are already being validated from aircraft 

altitudes, but may be applied to data from future spacebourne GPS receivers. Surface 

wind data from radiosondes were used for comparison. This experiment was a 

cooperative project between NASA, the lEEC in Barcelona, and the University of 

Colorado at Boulder. 

1.0 INTRODUCTION 

Measurement and tracking of ocean storm strength and sea state are important elements in the 
monitoring of natural hazards. Radar remote sensing instruments, such as scatterometers have been used 
as surface wind speed measurements. These techniques have developed from the first aircraft observations 
relating the backscatter coefficient to wind speed in the late 1960s, through the first satellite demonstration 
on Skylab in 1973 (Ulaby et al., 1981, pp 10) to the present state of the art instruments; NSCAT 
(Graf et al., 1998) and QuickSCAT. Altimeters can also generate a measurement of wind speed and 
significant wave height from the shape of a reflected pulse. 

Recently, an new remote sensing technique has been developed (Garrison et al., 1998) which uses 
the (forward) scattered signal from satellite navigation systems such as GPS or GLONASS. The earliest 
work on this concept, with an emphasis on altimetry, can be found in (Martiin-Neira, 1993), 
(Martiin-Neira, 1996). This wind vector retrieval technique measures the change in shape of the 
cross-correlation function between the reflected signal and the reference PRN code. This cross-correlation 



has been shown to be related to the mean square facet slope of the reflecting surface 

(Zavorotny and Voronovich, in press), which has long been known to be empirically related to surface wind 
speed (Cox and Munk, 1994). Subtle anisotropy in the cross-correlation function may also be used to 
determine the wind direction if a sufficient signal to noise ratio is available (Armatys, 1999). 

The use of the forward scatter of the GPS signal in this application has several advantages over 
conventional scatterometers. It requires only a low-power (lOW) receiver as user equipment, (as compared 
to 198 W for the Sea Winds instrument (Graf et al., 1998)) This instrumentation could reuse a significant 
amount of hardware already developed and space qualified for GPS navigation use (for example, the 
airborne GPS receivers used to date have been un-modified navigation hardware with only software 
changes, coupled with reversed polarity antennas). Furthermore, reflected power is not directly measured, 
rather the shape of the cross-correlation function is fitted against analytical or empirical models. The 
"process gain" of spread spectrum signal allows the extraction of useful data from an extremely weak 
signal. For these two reasons, reflected GPS technique can use uncalibrated omni-directional antennas 
several centimeters in diameter to collect scientifically valid data from aircraft altitudes. Making use of 
reflections from 10 or more visible GPS satellites would give a wide distribution of data samples. The use 
of low gain antennas, as described above, could provide this coverage without the need for scanning. 

Analytical models have already been developed and have compared well against the large body of 
existing data, collected mostly from aircraft at altitudes below 8Km. These models have been used both as 
the basis of wind vector retrieval algorithms (Komjathy et al., in press) (Garrison et al., in review), as 
well as extrapolated to predict the signal to noise ratio from a similar instrument used in low earth orbit. 
Retrievals have shown a precision of 2 meters/sec in comparison to TOPEX, providing confidence in these 
models when apphed to aircraft altitudes and velocities. Extrapolation of these models to satellite altitudes 
and velocities, however, has never been demonstrated with experimental data. It was therefore determined 
that collection of a segment of data from very high altitudes would serve to validate these models, and the 
joint NASA-IEEC MEBEX project was formulated with this purpose. 



2.1 RECEIVER DESCRIPTION 

The receiver used for these experiments was a modification of the Delay-Mapping receiver originally 
used on aircraft flight campaigns (Komjathy et al., 1999). A schematic of this receiver is shown in Figure 
1. Two RF front-ends are used, one is fed by a conventional right-hand circularly polarized (RHCP) 
antenna oriented to receiver direct line of sight GPS signals, the other is connected to an left-hand 
circularly polarized (LHCP) antenna oriented to receive the reflected signals. Whereas the direct signals 
are tracked using delay-lock loops and carrier frequency lock loops as on any navigation receiver, the 
reflected signal is not tracked at all. Rather, an array of correlator channels are controlled "open-loop" to 
process the reflected signal and generate correlation power (sum of the In-phase squared and Quadrature 
squared) at a range of code delays relative to the direct line of sight signal. These post-correlation samples 
are then averaged to reduce data rates, and stored to a solid state hard drive. The receiver hardware is 
based around a commercially available GPS development system (Plessey, 1995), and is controlled by a 
PC-104 based real time computer. 

Additional complications were present due to the requirement that the system operate 
autonomously, and be able to re-start at unknown times in the trajectory. This was accomplished with 
minimal modifications to the existing code, and setting up scripts to cyclically call the following sequence: 

• Cold start sequence, Total duration of 30 minutes; Searches for satellites and determines an initial 
position estimate, and identifies highest elevation satellite. This will allow the receiver to initialize 
without any prior knowledge of position. 

• Parallel Delay Mapping Receiver: Specular Tracking (PDMR-Spec) , 20 minutes, given the highest 
elevation satellite, collects continuous data in 12 correlators, separated at 1/2 code chip intervals, set 
near the specular point location predicted for the highest elevation satellites. Tracking of the direct 
line of sight signal for the same PRN must also be maintained during this time in order to properly 
align the code phase for the reflection channels. 100 samples of waveform data are averaged and 
stored to disk at a rate of 10 Hz. 



, P arallel Delay Mapping Receiver: Search (PDMR-Search) , 20 minutes, same correlator architecture 
as PDMR-Spec. No prior knowledge of vehicle position and path length delay are assumed, however. 
Searches delay space by slewing code delay in the reOected channels by 1/2 code chip increments 
until the power in bin no. 4 exceeds a threshold. A simple controller is then used to maintain 
maximum power in bin number 4 by incrementing code phase by plus or minus half-code chips. 

• Serial Delay Mapping Receiver: SDMR , 60 minutes, tracks up to six direct satellites through the 
direct RF front end. Uses this information to assign each of the lower RF front ends to the same 
satellite, and then sequentially steps through relative delays in the lower channels in 1/2 code chip 
steps. This code has the advantage of the PDMR in that it only requires an external position 
solution (from Cold Start) to initiahze its warm start. Once 4 or more satellites are tracked from 
direct signals, the SDMR will automatically predict specular point offsets and properly align the 
reflected signal channels. The disadvantage is in the reduced signal to noise ratio, because of 
sequencing through 32 delay steps once every 100 ms as opposed to the continuous averaging of 1ms 
accumulations at the same delay for the complete 100 ms time. This allows only l/lOO'^^ of the 
post-correlation integration time to be applied to each correlator which results in a signal to noise 
ratio reduction of about 1/10 (10 dB). In the SDMR, a new sample of the waveform is available at a 
rate of lOHz. These are passed through a moving average filter and written to disk at a rate of 1 Hz. 

• System Reboot 

The hardware used for this experiment was very similar to that flown for the much shorter duration 
Virginia Space Grant Consortium (VSGC) balloon launch in 1998 (Garrison and Katzberg, in press). 
Figure 2 shows a photograph of this instrument when it was assembled at the Langley Research Center for 
the VSGC experiment. 

The receiver front end uses an automatic gain control to maintain a constant mean value of the noise 
floor, however on the VSGC launch, a drift in this noise floor was observed when the balloon reached very 
high altitudes. It was suspected that this drift was the result of thermal effects on the receiver hardware. 



These effects were later reproduced using a thermal vacuum chamber in the Guidance, Navigation and 
Control Center of the NASA Goddard Space Flight Center, prior to the re-flight on GPSR-MEBEX. 
However, none of the data retrieved from the MEBEX experiment has shown these effects. This is 
probably the result of better insulation of the instrument on this flight as compared to the VSGC mission. 

2.0 EXPERIMENT PLAN 

The GPSR-MEBEX flight was made possible by payload space provided by the Agenzia Spaziale Italiana 
*/ Instituto Nacional de Tecnica Aerospacial + (ASI/INTA) Transmediterranean Balloon Campaign. The 
flight path of this experiment is indicated on the map in Figure 3. Launch took place at 20:22 UTC August 
2nd 1999. After a brief detour inland from local winds, the balloon crossed into the Mediterranean. 
Following a four hour ascent, the balloon maintained an altitude between 35 and 37 km for approximately 
14 hours as shown in the plot in Figure 4. Stratospheric winds took the experiment near the coast of Africa 
before returning to higher latitudes. It crossed over Spanish land at 35 Hours. The satellites recorded in 
the reflection channels for both SDMR and PDMR are indicated in Figure 5 

3.0 DATA REDUCTION 

Following the successful flight, the complete instrument was returned to GSFC where the solid state 
hard drive was removed and the raw (level 0) measurement data was retrieved and transferred to the 
University of Colorado for processing. Additional tracking data, collected by ASI using GPS was also 
available. 

The data post-processing performs the following functions to generate a set of level la data which 
will be released to the scientific community through a World Wide Web site maintained by the University 
of Colorado. 

• Numerically determine the location of the specular reflection point on the WGS-84 ellipsoidal model 
of the Earth. 



'Italian Space Agency 

^Spanish Institute for Aerospace Techniques 



• Compute the correct path length delay between a direct and reflected signal. 

. Translate the location of each delay bin, relative to the predicted delay to the specular point on the 
WGS-84 ellipsoid. 

To perform this function, the precise GPS ephemerides from the National Geodetic Survey (NGS) 
were used. An error in the receiver software on this mission prevented the satellite numbers used in the 
parallel receiver segments from being recorded. This information was later inferred during post-processing 
from the satellites selected and tracked during the previous cold start phase and then from the latter serial 
receiver phase. A check on proper selection of satellite number was made by comparing the location of the 
waveform in delay space relative to the specular point location. From this altitude, the path length 
difference between satellites at different elevations is very large, and if the wrong satellite was selected, 
then the waveform would be translated away from the specular point delay by much larger than a few 
chips. This served to validate selection of the correct satellite. 

4.0 PRELIMINARY RESULTS 

Waveforms from the level la data set were scaled to have a constant area at each sample time, t, 
according to: 

In which NF is the noise floor, and i is the sample from a single correlator. For these experiments, a 
measured noise floor, obtained from the average power in the first delay bin was used. Experience with the 
aircraft data shows that this serves to take out uncertainty in the calibration between the direct and 
reflected antennas as well as longer period variations which were observed in the total reflected power. 
All of the data presented in this paper was from the SDMR segments. 

5.1 RADIOSONDE DATA COMPARISON 



Waveform data collected from this experiment, and post-processed to the level la data format described 
above has been distributed to the experiment partners. Preliminary comparisons to radiosonde data 
collected at three different locations surrounding the Mediterranean Sea have already been made. These 
data were obtained from the NOAA Forecast Systems Laboratory 1. The analytical model from 
(Zavorotny and Voronovich, in press) was used to generate predictions of the waveform shape based upon 
the measured surface wind speeds from the radiosondes. Figures 7, 8 and 9 show the georeferenced, level la 
data from the SDMR during three segment indicated at the points on figure Figure 6. An exponential 
series of the form (Garrison and Katzberg, in press) 



r2(r)«exp 



N 



J2ai{T-Toy 



li=0 



(2) 



has has been fit to the measured waveform to obtain a "mean" shape over each segment of data. The 
location of these measurements relative to the location of the radiosonde stations, and the nearest wind 
speed measurement and time for each station are shown on Figure 6. 

These figures indicate that the analytical models match the experimentally measured trends. The 
present lack of simultaneous space and time measurements makes a more exact comparison difficult. It is 
hoped that soon QuickSCAT, TOPEX and meteorological data will be available to offer a better 
comparison. 

5.2 QUICKSCAT AND TOPEX COMPARISON 

Waveform data from three segments of the mission (indicated on Figure 3 in the previous section) in which 
the balloon ground track crosses the QuickSCAT ground track will be used for comparison against 
analytical model predictions given the windspeed retrievals from the QuickSCAT instrument. 

5.3 MM5 MODEL COMPARISONS 



^http://raob.fsl. noaa.gov/Raob_Software. html 



Another source of comparison truth data is presently being collected from the European Center for 
Medium-Range Weather Forecasts (ECMWF) §. It is expected that these data will be available in 
February, 2000. These measurements will be used to initialize the Pennsylvania State University/National 
Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) ^. which could extrapolate wind 
vector measurements to the time and location of the balloon flight path. 

5.0 CONCLUSION 

A large body of data has been collected on reflected GPS signal structure at very high altitudes. 
Preliminary investigation of this data agrees with the predictions of analytical models. Further work will 
have to be done to more closely compare these measurements to other sources of data in the area in which 
the flight was conducted. Future work includes comparison with MM5 analysis over the Mediterranean 
using ECMWF initialization boundary conditions data and comparison against satelhte data from 
altimeters and scatterometers. Results from this experiment will be used to improve the retrieval 
algorithms which would be used on future satellite based instruments. 



§ http://www.ecmwf.int 
'http://box.mmm.ucar.edu/mm5 



Bibliography 



M. Armatys, Receiver Algorithms for Estimation of Sea-Surface Parameters Using Reflected GPS Signals, 
Dissertation Prospectus, University of Colorado, Boulder, CO, December 21, 1999. 

C. Cox and W. Munk, "Measurement of the Roughness of the Sea Surface from Photographs of the Sun's 
Glitter," Journal of the Optical Society of America, Vol. 44, No. 11, pp 838-850, November, 1994. 

J. L. Garrison and S. J. Katzberg, "The Application of Reflected GPS Signals to Ocean Remote Sensing," 
Remote Sensing of Environment in press. 

J. L. Garrison, S. J. Katzberg, and M. I. Hill, "Effect of Sea Roughness on Bistatically Scattered Range 
Coded Signals from the Global Positioning System," Geophysical Research Letters, Vol. 25, No. 13, pp 
2257-2260, July 1, 1998. 

J. L. Garrison, A. Komjathy, V. U. Zavorotny, and S. J. Katzberg, "wind Speed Measurement from 
Bistatically Scattered GPS Signals," IEEE Trans. Geosci. Remote Sensing in press. 

J. Graf, C. Sasaki, C. Winn, W. T. Lui, W. Tsai, M. Freilich, and D. Long, "NASA Scatterometer 
Experiment," ACTA Astronautica, Vol. 43, No. 7, pp 367-407, 1998. 

A. Komjathy, J. L. Garrison, and V. U. Zavorotny, "GPS; A New Tool for Ocean Science," GPS World, 
pages 50-56, April, 1999. 



10 



A. Komjathy, V. U. Zavorotny, P. Axelrad, G. H. Born, and J. L. Garrison, "GPS Signal Scattering from 
Sea Surface: Wind Speed Retrieval Using Experimental Data and Theoretical Model," Remote Sensing of 
Environment in press. 

M. Martiin-Neira, "A passive refiectometry and interferometry system (PARIS): application to ocean 
altimetry," ESA Journal, Vol. 17, pp 331-355, 1993. 

M. Martiin-Neira, Altimetry Method, U.S. Patent, August 13, 1996. 

GPS Bulder-2 Designer's Guide, GEC Plessey Semiconductors, April, 1995. 

F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing: Active and Passive, volume 1 
Artech House, Inc., 1981. 

V. U. Zavorotny and A. Voronovich, "Scattering of GPS Signals from the Ocean With Wind Remote 
Sensing Application," IEEE Trans. Geosci. Remote Sensing in press. 



RK 

From- End 




Code Tracking 






Time and/or 
Posiiwn S.'tn 




Loop 


Code Phase and 








t 






Spttular 
Poini Delay 






Carrier Frequency 






Figure 1: Schematic of Delay Mapping Receiver 




Figure 2: Assembly of Delay Mapping Receiver Hardware 



WEBEX fiight. Color-coded altitude. Time in UTC. 




Figure 3: GPSR-MEBEX Flight Path 



40 



35 



30 



25- 



20 



15 



10 



"1 r 





i 
1 

1 



_1 L_ 



_I L_ 



18 20 22 



24 26 28 30 32 
GPS Time (Hrs from 8-2-99) 



34 36 38 



Figure 4: GPSR-MEBEX Altitude Profile 



35 



30 



25 



0. 
S 20 



x-K OGB^- 



15 



DC 



10 



-I 1 r 



X-K 
X-K 



20 



21 22 



-K OO O- 



-K 0£&^t- 



SDMR 
PDMR 



-*< OS^^t — X 



23 24 25 26 27 

GPS Time (Hrs from 8-2-99) 



28 29 30 



Figure 5: Satellite PRNS Recorded in the Reflection Channels 



38.2 



37.8 - 



d>37.6 



2 37.4 



37.2 




36.8 



12 14 



Longitude (deg.) 



Figure 6: Location and Time of Radiosonde Measurements Relative to Flight Path 



0.5 



0.4 



0.3 



0.2 



-0.1 



#i- 







1 2. 3 4 

Delay from Specular Pt. (chips) 



0.5 



0.4 



0.3 



S 

o 
Q. 



0.2 



0.1 



-0.1 




12 3 4 

Delay from Specular Pt. (chips) 



Exp. fit to Data 



Model 3 m/s 
Mode! 5 m/s 



Figure 7: Reflected GPS Waveform for 400 sec. of Data at Point A 




-1 



12 3 4 5 

Delay from Specular Pt. (chips) 



0.4 
0.35 

0.3 
0.25 

0.2 
0.15- 

0.1 

0.05 



-0.05 

-0.1 



Exp. ill 10 Data 
Model 5 m/s 




12 3 4 

Delay from Specular Pt. (cfiips) 



Figure 8: Reflected GPS Waveform for 600 sec. of Data at Point B 



0.6 




12 3 4 5 

Delay from Specular Pt. (chips) 



0.6 



0.5 



0.4 



I 0.3 

o 

Q. 



o 0.2 



0.1 



' 



-0.1 




-2 -1 



12 3 4 5 

Delay from Specular Pt. (cfilps) 



Exp. fil to Data 
Model 2 m/s 



Figure 9: Reflected GPS Waveform for 600 sec. of Data at Point C