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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.