anisotropy in ocean scattering of bistatic radar using signals of opportunity

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ANISOTROPY IN OCEAN SCATTERING OF BISTATIC

RADAR USING SIGNALS OFOPPORTUNITY

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

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Outline• Background

– Previous Work– Overview of XM Radio System– Motivation

• Objective• Airborne Experiment• Data Processing• Results• Summary and Future Work


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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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• 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]


Motivation

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XM System


– Rhythm (115oW)• Elevation: 31.3o

• Azimuth: 234.4o

– Blues (85oW) • Elevation: 41.6o

• Azimuth: 196.4o

• Two active geostationary satellites

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Objective

• Quantify retrieval anisotropy between two satellites at different azimuth

• Examine potential to retrieve wind direction from this effect


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Airborne Experiment


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Waveforms• Altitude of the aircraft: 3,471 meters (11,400 feet)• Cross-correlation of direct and reflected signals


• Sampling Frequency− 8MHz

• Coherent Integration− 10ms

• Incoherent Integration− 1sec5 10 15 20

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X: 12.13Y: 1

Flight Result, Fs = 8MHz, Altitude = 3174 meters

Lags[sec]

|Y(

k,0)|

2

X: 17.13Y: 1

Satellite "Blues"Satellite "Rhythm"

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Data Processing• Two-step estimation process [Garrison 2003]• First step:

– Isotropic normal PDF assumed– Nonlinear least squares estimation

= Mean Square Slope (MSS)

= Scale factor (remove variation in signal power)

= Delay offset (adjust small uncertainties in delay)– Independent estimate for each satellite


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Data Processing• Chesapeake Lighthouse (CHLV2): 7.5 m/s (MSS = 0.0010)


Rhythm: 0.0076 (6 m/s) Blues: 0.0098 (7.2 m/s)

12 13 14 15 16 170

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Lags[sec]

|Y(

k,0)|

2

Flight Estimation, Elevation = 31.3o, Azimuth = 234.4o

16 17 18 19 20 21 22 230

0.2

0.4

0.6

0.8

1

|Y(

k,0)|

2

Lags[sec]

Flight Estimation, Elevation = 46.3o, Azimuth = 196.4o

Data Processing• Second Step:

– 2-D normal slope PDF– Model fit to both satellites simultaneously– Isotropic MSS su

2+sc2 fixed to average from first step

– Cross-/up-wind ratio (sc/su) fixed to 0.85

– Principal axis α of PDF varied to minimize residuals

– Wind direction with respect to North

11IGARSS – Vancouver, Canada - July 24-29, 2011

|𝑌 𝑀 (𝜏𝑘 , ,0 ;𝜶 )|2=𝑆 ⟨𝑌 2 (𝜏𝑘−𝜏0 ,0 ;𝜎2𝑢 ,𝜎

2𝑐 ,𝛼 )⟩

𝜃𝑊=𝑎𝑧1,2−𝛼

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Results

• Minimum residual found at θW = 57o


0 20 40 60 80 100 120 140 160 1804.277

4.2775

4.278

4.2785

4.279

4.2795

4.28

4.2805

4.281

4.2815x 10

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Wind Direction from the North, in degrees

Res

idu

als

Sum of residuals from the two satellites

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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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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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Thank You!


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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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BACKUP AND EXTRAS


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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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Glistening Zone• Rhythm: Semi-major = 4.76km, Semi-minor = 2.47km• Blues: Semi-major = 5.40km, Semi-minor = 2.80km

IGARSS – Vancouver, CANADA- July 21-24, 2010

0 2 4 6 8 10 12 14-3

-2

-1

0

1

2

3

X [km]

Y [

km]

Glistening Zone

RhythmBlues

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Airborne Experiment: Geometry

• Experiment conducted: 02-July-2010 in Piper Navajo• Experiment time period: 07:51AM EDT - 09:19AM EDT


anisotropy in ocean scattering of bistatic radar using signals of opportunity

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