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UNCLASSIFIED
1
CLASSIFICATION
The NRL Multi Aperture SAR (NRL MSAR):System Description and Recent Results
Luke Rosenberg
Defence Science and Technology Organisation, Australia
Mark Sletten, Naval Research Laboratory, USA
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• Motion in SAR imagery
• Single vs. Multi-aperture SAR
• The Velocity SAR algorithm for focussing moving scatterers
• Demonstration of the VSAR algorithm using the NRL FOPAIR
• Initial results from the Airborne MSAR system
• Enhanced VSAR
• Future plans
Acknowledgements:
• Naval Research Laboratory, Remote Sensing Division:
Mark Sletten, Steve Menk, Jakov Toporkov, Bob Jansen
• Naval Research Laboratory, Radar Division:
Raghu Raj, Denny Baden
Outline
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Effects of Scene/Target Motion on SAR Signatures
• Relative motion between platform and scene is fundamental to SAR
• SAR processors assume scene is stationary: Scene motion causes distortion
• Constant radial motion: azimuthal offsets, a.k.a. “train off the track” distortion
• Radial acceleration and azimuthal motion: azimuth defocusing
• Issue is significant for marine applications, since complex motion is pervasive
• Signatures not only displaced, but smeared as well
Real Aperture Radar Image SAR Image (emulated)
NRL FOPAIR Imagery, Small boat on the Chesapeake Bay
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Formation of a Standard SAR Image
1 phase center
Tim
e
Space
Synthetic aperture
Road Cars
Image
Azimuth
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Formation of an MSAR Image Stack
M phase centers
Tim
e
Space
Road Cars
Image
Azimuth
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Formation of an MSAR Image Stack
M phase centers
Tim
e
Space
Image Time Stack
Azimuth
𝑡 = 𝑡0
𝑡 = 𝑡0 + ∆𝑡
𝑡 = 𝑡0 + 2∆𝑡
𝑡 = 𝑡0 + 3∆𝑡
𝑡 = 𝑡0 + (𝑀 − 1)∆𝑡
Road Cars
• Images look the same: motion information is in the phase of the complex pixels• Images look the same: motion information is in the phase of the complex pixels
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Velocity SAR (VSAR) Processing
• Doppler processing converts the image time-stack into a velocity stack
• Shifting each velocity image by 𝑅
𝑉𝑝𝑣𝐷𝑜𝑝 corrects azimuthal misplacement
• An incoherent sum down the corrected velocity stack forms a single corrected image
Time Stack Velocity Stack Shifted Velocity Stack
FF
TAzimuth
Azimuth Azimuth
Dopple
r Fre
quency/v
elo
city
Corrected Image
(Incoherent sum)
Azimuth
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NRL FOPAIR
• NRL Focused Phased Array Imaging Radar (NRL FOPAIR)
• Updated version of UMass FOPAIR (McIntosh and Frasier, 1995)
• Mimics a SAR: Receive array elements rapidly and sequentially scanned
• Generates image time-stacks with a high frame rate (780 fps “movies”)
• X-band (9.85 GHz) fully polarimetric, 200 MHz BW (0.75 m resolution)
• 16-module receive array easy to reconfigure
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M Apertures
deff
FOPAIR as an MSAR Test BedT
ime
Space
MSAR FOPAIR
Tim
e
Space…
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SAR Image (emulated)
First Reported Demonstration of VSAR-Based Signature
Correction
• NRL FOPAIR imagery of a small boat used to demonstrate VSAR signature correction
Sletten, IEEE Trans. Geoscience Remote Sens., Vol. 51, No. 5, May 2013
VSAR Image (emulated)
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• X-band (9.875 GHz CF)
• Bandwidth: 220 MHz
• Waveform: LFM, both up and down chirps
• Peak and average power: 1.4 kW, 210 W
• Phase centers: 32 along-track
• Polarization: VV
• Platform: Saab 340
• IMU: Novatel
• Data recorder: NRL custom-built, 4-channel, 800 MB/s sustained
~ 1200 m
~ 1.5 km
22°45°
NRL MSAR Basic Specifications
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Saab 340
Tx Down-chirp Tx Up-chirp
Rx 1-16
Novatel IMU (behind Rx modules)
NRL MSAR Aircraft and Radome
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• Use two transmit antennas to double number of phase centers
• Minimum and maximum unambiguous velocities, assuming VSAR-type processing:
At Vp=70 m/s (Saab 340)
• Cycle through all 32 combinations of Tx and Rx antennas in 320 microsec (8 pulses)
32 Resulting Phase
Centers
2 Transmit Horns
16 Physical Receive
Elements
deff
deff ≈ d/2 = 5.25 cm
smd
Vv
eff
p /104max
sm
Md
Vv
eff
p /7.02min
d=10.5 cm
32 Phase Center Array
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Receive
Elements
Switches
U D
Antenna Switching Schematic
Up chirp transmit antenna Down chirp transmit antenna
Data
acquisition
channel 1
Data
acquisition
channel 2
Data
acquisition
channel 3
Data
acquisition
channel 4
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Receive
Elements
Switches
U
Pulse 1
Up-chirp
Receive elements 1, 9, 17, 25
Data
acquisition
channel 1
Data
acquisition
channel 2
Data
acquisition
channel 3
Data
acquisition
channel 4
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Receive
Elements
Switches
D
Pulse 2
Down-chirp
Receive elements 1, 9, 17, 25
Data
acquisition
channel 1
Data
acquisition
channel 2
Data
acquisition
channel 3
Data
acquisition
channel 4
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Receive
Elements
Switches
U
Pulse 3
Up-chirp
Receive elements 3, 11, 19, 27
Data
acquisition
channel 1
Data
acquisition
channel 2
Data
acquisition
channel 3
Data
acquisition
channel 4
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Receive
Elements
Switches
D
Pulse 4
Down-chirp
Receive elements 3, 11, 19, 27
Data
acquisition
channel 1
Data
acquisition
channel 2
Data
acquisition
channel 3
Data
acquisition
channel 4
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Receive
Elements
Switches
U
Pulse 5
Up-chirp
Receive elements 5, 13, 21, 29
Data
acquisition
channel 1
Data
acquisition
channel 2
Data
acquisition
channel 3
Data
acquisition
channel 4
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Receive
Elements
Switches
D
Pulse 6
Down-chirp
Receive elements 5, 13, 21, 29
Data
acquisition
channel 1
Data
acquisition
channel 2
Data
acquisition
channel 3
Data
acquisition
channel 4
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Receive
Elements
Switches
U
Pulse 7
Up-chirp
Receive elements 7, 15, 23, 31
Data
acquisition
channel 1
Data
acquisition
channel 2
Data
acquisition
channel 3
Data
acquisition
channel 4
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Receive
Elements
Switches
D
Pulse 8
Down-chirp
Receive elements 7, 15, 23, 31
Data
acquisition
channel 1
Data
acquisition
channel 2
Data
acquisition
channel 3
Data
acquisition
channel 4
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• 30 flight hours over the span of 2+ weeks, September 2014.
• Based out of Newport News VA.
• After a difficult installation, system worked remarkably well. Some issues:
• Due to placement of transmit horns, only 28 unique phase centres.
• Mismatch with up-chirp / down-chirp waveforms - produced low image
coherence. Current VSAR results are restricted to 16 phase centres.
• Two subjects of study
• Oregon Inlet on the Outer Banks of NC
• Imaged boats of opportunity, waves, currents, vehicles.
• Used linear flight patterns (i.e. strip-map).
• Cooperative vessels in the Southern Chesapeake Bay.
• Imaged 30 different vessels, both stationary and moving (0-50 kts).
• Used both linear and circular flight patterns.
Inaugural NRL MSAR Deployment
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VSAR analysis region
Inaugural NRL MSAR Deployment
Oregon Inlet, NC Outer Banks
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First VSAR Analysis
Animation:
Click to start
Shoaling
waves
Vehicles
Northbound
Southbound
• VSAR processing significantly reduces smearing of shoaling waves
• (Faint) vehicle signatures shifted back to bridge
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Velocity Image Movie
Animation:
Click to startEach image shifted by
𝑹
𝑽𝒑𝒗𝑫𝒐𝒑 to
correct azimuthal displacement
Vehicles
• Vehicle signatures much more visible than in previous composite image, due to
Doppler filtering inherent in VSAR processing
• Vehicle speeds projected onto bridge are 64 and 48 mph (speed limit 55 mph)
Northbound
Southbound
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MSAR processing chain
• Radar collects RAW data – large binary files (~60-160 GB)
• 1st stage processing (MATLAB):
• Extracts the 32 phase centres.
• Baseband conversion and low pass filter.
• Creates single file for each phase centre (~1-5 GB).
• 2nd stage processing (C-code / JAVA front end):
• SAR image formation uses chirp scaling algorithm.
• Includes range compression and integrated motion compensation.
• Creates SAR image for each phase centre.
• 3rd stage processing (MATLAB):
• Extract small region for processing.
• Channel balancing.
• VSAR processing.
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Adaptive channel balancing
• VSAR processing assumes SAR magnitude images are identical and there is a reasonable
level of coherency between complex images.
• Implemented adaptive 2D calibration technique for the SAR images*.
• Works in image frequency domain.
• First stage estimates and corrects channel ‘transfer’ function along each spatial
frequency dimension.
• Second stage required to balance magnitude in the image domain.
• Example below shows distribution of the coherence and magnitudes before / after channel
balancing.
* Ender, J. H. G. ‘The airborne Experimental Multi-Channel SAR System AER-II’, European
SAR conference, 1996, pp. 49-52.
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VSAR image improvement
• Problems identified:
1. Spectral leakage from non-DC velocity images.
2. Velocity components hard to distinguish after non-coherent sum.
3. Loss of dynamic range in VSAR image.
• Solutions:
1. Identify strong scatterers in the DC velocity image and mask these
pixels in the other velocity images – threshold set as mean of the
DC image.
2. Balance the means of the different velocity images relative to the
DC velocity image.
3. Need to mask non-significant scatterers present in each non-DC
velocity image – threshold set as 2-6 std above mean for each
image.
4. Further improvements – maximum velocity image and
autoregressive spectral estimate.
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First VSAR image
• Original VSAR image with no extra processing.
dB
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Improved VSAR Image
dB
• Improved VSAR image with extra processing - filtering removed some details.
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Maximum VSAR Image
dB
• Maximum improved VSAR image with extra processing (some extra detail).
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Improved spectral estimate
dB
• Final improvement was to introduced a 4th order auto-regressive spectral estimate to
improve velocity resolution / dynamic range.
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Visualisation – Velocity overlay
• Find the dominant velocity component in each pixel and overlay it on the VSAR image.
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Visualisation - 3D Image slice
Breaking wave with
5x5 smoothing window
Cars detected on bridge
- velocities approximately
match speed limit of 55 mph
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Current / planned work
VSAR
• Improve coherency across array – utilise 28 unique channels.
• Investigate VSAR images of charted boats.
• Develop backprojection code for the circular spotlight mode.
• Investigate Velocity ISAR algorithm.
Beamforming
• Modelling of expected performance using the aperture switching scheme.
• Application of adaptive processing schemes to suppress clutter and detect
targets – i.e. pre / post Doppler STAP.
Follow on trial
• Trial planned for October 2015 focussing on Langmuir Turbulence / small
target detection.
• Opportunity to test polarimetric MSAR.