This document summarizes an experiment that measured anisotropy in ocean scattering using signals from two XM Radio satellites. Airborne radar measurements were taken off the coast of Virginia from two satellites with different azimuth angles. The data was processed and found minimum residuals at a wind direction of 57 degrees, compared to reported wind direction of 28 degrees. Future work will involve fitting both satellite data to a single wind model and using more accurate surface wind measurements.

1. ANISOTROPY IN OCEAN
SCATTERING OF BISTATIC
RADAR USING SIGNALS OF
OPPORTUNITY
Rashmi Shah*, Dr. James Garrison*, Dr. Michael Grant**
*School of Aeronautics and Astronautics,
Purdue University
**NASA Langley Research Center
IGARSS – Vancouver, Canada - July 24-29, 2011 1

2. Outline
• Background
– Previous Work
– Overview of XM Radio System
– Motivation
• Objective
• Airborne Experiment
• Data Processing
• Results
• Summary and Future Work
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3. Previous Work
• Global Navigation Satellite System Reflectometry (GNSS-R)
– First demonstration of remote sensing with “signals of
opportunity”
– Retrieval of ocean surface roughness, wind speed, soil
moisture, and ice
– 15 years of development: airborne and space (UK-DMC)
– GNSS-R enabled by use of known pseudo-random noise
(PRN) code
• Digital Communication Signals Reflectometry
– Expand methods to other “signals of opportunity”
– Demonstrated with XM digital radio
• Commercial satellite radio system in the US
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4. Motivation
• Why Digital Communication Signals?
– Multiple frequency bands: Different sensitivity
– Higher transmitted power: Better accuracy
– Located in geostationary orbit: Fixed geometry
• Challenge:
– No a priori knowledge of data bits
• Hypothesis:
– Data bits are long, random, uncorrelated stream
• Hypothesis verified using XM digital radio signals (S-band)
[Shah, et al, IGARSS 2010]
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5. XM System
• Two active geostationary satellites
– Rhythm (115oW) – Blues (85oW)
• Elevation: 31.3o • Elevation: 41.6o
• Azimuth: 234.4o • Azimuth: 196.4o
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6. Objective
• Quantify retrieval anisotropy between two satellites at
different azimuth
• Examine potential to retrieve wind direction from this effect
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7. Airborne Experiment
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8. Waveforms
• Altitude of the aircraft: 3,471 meters (11,400 feet)
• Cross-correlation of direct and reflected signals
Flight Result, Fs = 8MHz, Altitude = 3174 meters
X: 12.13 X: 17.13
Y: 1 Y: 1
1 Satellite "Blues"
Satellite "Rhythm"
0.9
0.8
0.7
• Sampling Frequency
2
0.6
|Y(k,0)|
− 8MHz
0.5
0.4
0.3 • Coherent Integration
0.2 − 10ms
0.1
• Incoherent Integration
− 1sec
0
5 10 15 20
Lags[sec]
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9. Data Processing
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10. Data Processing
• Chesapeake Lighthouse (CHLV2): 7.5 m/s (MSS = 0.0010)
Flight Estimation, Elevation = 31.3o, Azimuth = 234.4o Flight Estimation, Elevation = 46.3o, Azimuth = 196.4o
0.8 1
0.7
0.8
0.6
0.5 0.6
|Y(  ,0)|2
|Y(  k,0)|2
k
0.4
0.4
0.3
0.2
0.2
0.1
0
0 16 17 18 19 20 21 22 23
12 13 14 15 16 17 Lags[sec]
Lags[sec]
Rhythm: 0.0076 (6 m/s) Blues: 0.0098 (7.2 m/s)
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11. Data Processing
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12. Results
• Minimum residual found at θW = 57o
x 10
8 Sum of residuals from the two satellites
4.2815
4.281
4.2805
4.28
Residuals
4.2795
4.279
4.2785
4.278
4.2775
4.277
0 20 40 60 80 100 120 140 160 180
Wind Direction from the North, in degrees
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13. Results
• CHLV2 reported that the wind direction was 28o
• Discrepancy can be due to:
– CHLV2 & measurement location separated by 80km
• Closest Buoy (44014) not reporting data
– Azimuth separation between satellites only 38o - may
give reduced sensitivity
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14. Summary and Future Work
• Summary
– Anisotropy gives equivalent wind speed difference of
1.2m/s
– Wind direction may contribute to this effect
– Difference between principal axis of wind speed was 29o
• Future Work
– Single model fit to both satellites data
– Find better surface truth values:
• Model runs at measurement location
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15. Thank You!
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16. References
J.L. Garrison, A. Komjathy, V.U. Zavorotny, and S.J. Katzberg, “Wind speed
measurement using forward scattered GPS signals,” IEEE Transactions
on Geoscience and Remote Sensing, vol. 40(1), pp. 50–65, 2002.
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,” Geophys. Res. Lett, vol. 25(13), pp. 2257–2260, 1998.
E. Cardellach and A. Rius, “A new technique to sense non-Gaussian features
of the sea surface from L-band bi-static GNSS reflections,” Remote
Sensing of Environment, vol. 112, no. 6, pp. 2927 – 2937, 2008.
R. Shah, J.L. Garrison, M.S. Grant, and S.J. Katzberg, Analysis of correlation
properties of digital satellite signals and their applicability in bistatic
remote sensing,” Proceedings of the 2010 IEEE International Geoscience
and Remote Sensing Symposium, pp. 4114–4117, July 2010.
J.L. Garrison, “Anisotropy in reflected GPS measurements of ocean winds,”
Proceedings of the 2003 IEEE International Geoscience and Remote
Sensing Symposium, pp. 4480–4482, July 2003.
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17. BACKUP AND EXTRAS
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18. Previous Work
• Waveform generated by cross-correlating ocean-reflected
signals with locally generated pseudorandom code was
used
• Methods used:
– Matched Filters (Garrison, et al 1998)
– Nonlinear least squares parameter estimation (Garrison, et al 2002)
– Discretized approximation of the full-slope PDF (Caedellach, et al
2008)
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19. Glistening Zone
• Rhythm: Semi-major = 4.76km, Semi-minor = 2.47km
• Blues: Semi-major = 5.40km, Semi-minor = 2.80km
Glistening Zone
3
Rhythm
Blues
2
1
Y [km]
0
-1
-2
-3
0 2 4 6 8 10 12 14
X [km]
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20. Airborne Experiment: Geometry
• Experiment conducted: 02-July-2010 in Piper Navajo
• Experiment time period: 07:51AM EDT - 09:19AM EDT
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