Transcript Slide 1

Abigail Fuentes
Inerys Otero
INEL 5326
Prof. Domingo Rodríguez
07/07/2015
Objective
 Synthetic Aperture Radar (SAR) Imaging
Processing
 Design Method
 SAR Imaging Formation Hardware Design
 SAR Image Formation Testbed Environment
 SAR Image formation on TMS320C6711
 SAR Image formation on TMS320C6713
 Image Formation Results
 Conclusions
 References

 Implement
an algorithm for synthetic
aperture radar (SAR) image formation on the
TMS320C6711 and TMS320C6713 digital signal
processing (DSP) boards.
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Consists of forming an image of a landscape or terrain
surface using active sensing.
An antenna transmits and receives a series of pulse signals
reflected from an area of interest.
The antenna is placed on a moving platform
 Aircraft
 Satellite
Azimuth direction is defined to be in the same direction
parallel to the antenna.
Range direction is perpendicular to the azimuth direction.
A = azimuth direction
R = range direction
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Raw Data – The signals that are reflected from the
surface area form a reflectivity pattern. A
convolution operation is performed between the
reflectivity pattern and the impulse response
function that characterizes the image formation
system. This operation produces a two-dimensional
raw data.
Range compression – A matched filter is define in
terms of a range reference function which takes in
consideration the sampling rate, the duration of the
transmitted signal and the frequency modulation
rate of radar pulse. The convolution is performed
between each line of SAR data and the filter.
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Corner turning – Performs the transpose of a given
matrix.
Azimuth compression – The azimuth reference
function is characterized by the duration in which the
target is maintained illuminated by the antenna
beam, the phase variation detected in the received
signal, and the pulse repetition frequency. The
convolution is performed between each line of data
and the block reference functions.
Range compression
Azimuth compression
Laboratory Computer used
for the implementation on the
TMS320C6711
Laboratory Computer used
for the TMS320C6713
System
Windows XP Professional
Version 2002
Windows XP Professional
Version 2002
Computer
Dell Notebook D600
Intel (R) Pentium (R) M
processor 2.0 GHz,
599MHz, 1.00GB of RAM
Dell Notebook D600
Intel (R) Pentium (R) M
processor 2.0 GHz,
599MHz, 1.00GB of RAM
Code Composer Studio
Version
CCS-DSK 2 (`C600)
V2.21
Raw Data
Data Compressed in Range
Data Compressed in Range
Raw Data
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Applying Corner Turning to Data
Compressed in Range Direction
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Data Compressed in Azimuth
Data to be compressed in Azimuth
Data compressed in Azimuth Direction
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Raw Data
Data Compressed in Range
Data Compressed in Range
Raw Data
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Applying Corner Turning to Data
Compressed in Range Direction
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Data Compressed in Azimuth
Data compressed in Azimuth Direction
Data to be compressed in Azimuth
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Raw Data
Data Compressed in Range
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Applying Corner Turning to Data
Compressed in Range Direction
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Data Compressed in Azimuth
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Raw Data
Data Compressed in Range
Raw Data
Data Compressed in Range
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Applying Corner Turning to Data
Compressed in Range Direction
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Data Compressed in Azimuth
Data compressed in Azimuth Direction
Data to be compressed in Azimuth
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Raw Data
Data Compressed in Range
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Applying Corner Turning to Data
Compressed in Range Direction
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Data Compressed in Azimuth
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Raw Data
Data Compressed in Range
Raw Data
Data Compressed in Range
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Applying Corner Turning to Data
Compressed in Range Direction
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Data Compressed in Azimuth
Data to be compressed in Azimuth
Data compressed in Azimuth Direction
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Raw Data
Data Compressed in Range
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Applying Corner Turning to Data
Compressed in Range Direction
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Data Compressed in Azimuth
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 For
the TMS320C6711 DSP board, raw data of
size 128x128 were processed, whereas for
the TMS320C6713 DSP board, image
formation for raw data of sizes 128x128,
256x256, and 512x512 was achieved.
 For raw data of size 512x512, the images
were formed with more details and could be
appreciated better, in comparison with raw
data of smaller sizes.
 Results obtained from testbed were similar
to those obtained in MATLAB by Ana Ramírez.

Ana Beatriz Ramirez Silva, “On Implementation Time-Frequency Representations on
Hardware/Software Computational Structures for SAR Aplications”, University of
Puerto Rico, Mayagüez Campus, June 2006
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Cihan Erba, “SAR Raw Data Aspects and Focusing via High Precision Algorithms”,
Istanbul Technical University Electronics and Communication Engineering
Department Maslak, Istanbul, Turkey, IEEE 2003.
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Natural Resources Canada, “SAR Image Formation”.
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Peter T. Gough and David W. Hawkins, “Unified Framework for Modern Synthetic
Aperture Imaging Algorithms”, Department of Electrical and Electronic Engineering,
University of Canterbury, Private Bag 4800, Christchurch, New Zealand, Vol. 8, 343–
358 (1997), Received 12 August 1996; revised 18 January 1997.
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Bruce Walker, Grant Sander, Marty Thompson, Bryan Burns, Rick Fellerhoff, and Dale
Dubbert, “A High-Resolution, Four-Band SAR Testbed with Real-Time Image
Formation”, Sandia National Laboratories, Albuquerque, New Mexico.
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European Space Agency, “Chapter 1: The ASAR User Guide”, available from World
Wide Web: <http://envisat.esa.int/handbooks/asar/CNTR1.htm#eph.asar.ug>.
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Amit Aggarwal and Erlend Hansen, “Radar in general”, The Connexions Project,
Houston Texas, available from World Wide Web:
http://cnx.org/content/m11718/latest/