Synthetic Aperture Radar Imaging Using a Small Consumer Drone

Chenchen J. Li and Hao Ling

The University of Texas at Austin

1

I. Motivation and Objective

II. Drone SAR System

III. Validation on Corner Reflectors

IV. Preliminary Data on Other Targets

V. Conclusion

2

Outline

• Small drones have become popular for aerial photography.

3

Motivation

• What happens when you mount a radar on the drone?

4

Motivation

??? ???

• Small drones have become popular for aerial photography.

• Radar imaging could provide complementary information and extended operating conditions.

• Applications in scientific, agricultural, and environmental monitoring.

• UAV-SAR systems exist but are typically too heavy and need to be supported by large UAVs [1-3].

5

Motivation

[2] Koo et al., 2012.

44 lb system

[1] Lynx, Sandia, 2000.

125 lb system

[3] NanoSAR, BYU, 2006.

2-3 lb system

Develop and demonstrate a low-cost SAR system that is mounted on a small consumer drone (DJI Phantom 2).

Scientific Question: Can a low-cost, high-resolution SAR system be realized on a small consumer drone (whose maximum payload is typically less than 1 lb)?

6

Objective

Radar

• PulsON 410 (P410) radar by Time Domain Corporation.

• Board is 7.6 cm x 8 cm x 1.6 cm, weighs 58 grams, and can be battery powered.

• Emits short pulses at a pulse repetition frequency of 10 MHz.

• Equivalent frequency bandwidth from 3.1 to 5.3 GHz centered at 4.3 GHz.

• USB interface to control radar and transfer range profiles.

7

PulsON 410 UWB Radar

8

Drone SAR System

SAR system consists of P410 radar, Raspberry Pi + Wi-Fi Dongle, helix antennas mounted on aluminum ground planes. Entire system (including cables and batteries) weighs less

than 300 g.

Wi-Fi

Raspberry Pi

Computer

Time Domain Radar

Radar Control

Drone Control

Rmin

R ϕ

9

Drone SAR Prototype Photos

Helix Antennas

Radar

Raspberry Pi

• Broadband 5-turn helix antenna centered at 4 GHz.

• Supported by 3-D printed mold.

• Aluminum ground planes.

• 1 right-hand CP for Tx, 1 left-hand CP for Rx.

• Gain: ~10 dB ; Two-way 3 dB beamwidth: ~15°

10

Helix Antennas

2.5 3 3.5 4 4.5 5 5.5-30

-25

-20

-15

-10

-5

0

Freq (GHz)

S11 (

dB

)

2.5 3 3.5 4 4.5 5 5.50

5

10

15

Freq (GHz)

Ga

in (

dB

)

• Measured range profiles are real valued but finely sampled in time.

• Use FFT to get the complex frequency response and only keep data from 3.1-5.3 GHz.

11

Measurement Range Profiles

0 0.5 1 1.5 2 2.5-1500

-1000

-500

0

500

1000

1500

Down-range (m)

Sig

na

l S

tre

ng

th

-5 0 50

0.5

1

1.5

2

2.5

3x 10

4

Frequency (GHz)

Sig

na

l S

tre

ng

th

Raw Range Profile Frequency Response

• 4 small aluminum trihedrals as point-scatterer targets.

• Range profiles collected (at 20 Hz) by flying drone in a straight line across measurement scene.

• Prominent persistent scatterers facilitate range alignment for image formation.

12

Validation on Corner Reflectors

• Range profiles show significant range migration.

• Align to closest scatterer.

• Good agreement with point-scatterer simulation.

13

Motion Compensation

Ra

ng

e (

m)

Time (s)

0 1 2 3 4

8

9

10

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12

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Time (s)

Ra

ng

e (

m)

0 1 2 3 4-1

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Ra

ng

e (

m)

Time (s)

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Time (s)

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m)

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Measurement Simulation

Raw Sinogram

Aligned Sinogram

• Frequency/angle data are placed in k-space.

• Polar reformat to uniform kx-ky space and take 2-D inverse fast Fourier transform to obtain image.

14

Image Formation

𝐼𝑚𝑎𝑔𝑒 𝑟, 𝑐𝑟 = 𝐸𝑠 𝑓, ϕ 𝑒𝑗𝑘𝑥𝑟𝑒𝑗𝑘𝑦𝑐𝑟𝑑𝑘𝑥𝑑𝑘𝑦

𝑤ℎ𝑒𝑟𝑒

𝑘𝑥 =4𝜋𝑓

𝑐cosϕ

𝑘𝑦 =4𝜋𝑓

𝑐sinϕ

;

kx

ky

k-space data

2-D iFFT SAR image

Ran

ge

Cross-Range

ϕ = cos−1𝑅𝑚𝑖𝑛𝑅

• 3 focused scatterers. Cross-range smearing of farthest scatterer due to near-field effects.

• Good agreement between simulation and rail-SAR.

• More blurred result in drone-SAR.

15

Resulting SAR Images

Simulation Rail-SAR Drone-SAR

Ra

ng

e (

m)

Cross-Range (m)

-2 -1 0 1 2-1

0

1

2

3

4

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0

Ra

ng

e (

m)

Cross-Range (m)

-2 -1 0 1 2-1

0

1

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0

Ra

ng

e (

m)

Cross-Range (m)

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0

• Trihedral is left in the scene for reference.

• Stationary vehicle and human targets.

• Able to generate SAR images of other targets.

16

Application to Other Targets

Ra

ng

e (

m)

Cross-Range (m)

-2 -1 0 1 2-1

0

1

2

3

4

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0

Vehicle

Trihedral

• Trihedral is left in the scene for reference.

• Stationary vehicle and human targets.

• Able to generate SAR images of other targets.

17

Application to Other Targets

Ra

ng

e (

m)

Cross-Range (m)

-2 -1 0 1 2-1

0

1

2

3

4

-30

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-5

0

Vehicle

Trihedral Ra

ng

e (

m)

Cross-Range (m)

-2 -1 0 1 2-1

0

1

2

3

4

-30

-25

-20

-15

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-5

0

Vehicle

Trihedral

• Trihedral is left in the scene for reference.

• Stationary vehicle and human targets.

• Able to generate SAR images of other targets.

18

Application to Other Targets

Ra

ng

e (

m)

Cross-Range (m)

-2 -1 0 1 2-1

0

1

2

3

4

-30

-25

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-15

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0

Vehicle

Trihedral Ra

ng

e (

m)

Cross-Range (m)

-2 -1 0 1 2-1

0

1

2

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4

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Vehicle

Trihedral Ra

ng

e (

m)

Cross-Range (m)

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0

1

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Trihedral

Human

• Devised a portable, lightweight SAR system that can be mounted on a small drone.

• Verified its imaging capability on trihedrals and then collected preliminary data of other targets.

• Current work.

– Near-field correction

– Downward-looking SAR

19

Recap

• Previous: used far-field FFT-based imaging scheme on near-field data.

• Solution: apply near-field backprojection imaging scheme.

20

Near-Field Effect Correction

Near-Field Data

Near-Field Backprojection

Far-Field FFT-Based

Imaging Final

Image

• Matched filter algorithm that projects the scattered field data into the phase function.

21

Near-Field Backprojection

SAR 𝑟, 𝑐𝑟 =1

𝑁𝑀 𝐸𝑠 𝑥𝑛, 𝑓𝑚 × 𝑒

𝑗4𝜋𝑓𝑚𝑐 𝑐𝑟−𝑥𝑛

2+ 𝑟+𝑦𝑜2+𝑧𝑜2

𝑀

𝑚=1

𝑁

𝑛=1

SAR Image

cr

r

• Near-field effects have been removed.

• Good agreement between rail-SAR and simulation.

• More blurred result in drone-SAR.

22

Resulting SAR Images

Ra

ng

e (

m)

Cross-Range (m)

-2 -1 0 1 2-1

0

1

2

3

4

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-25

-20

-15

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-5

0

Ra

ng

e (

m)

Cross-Range (m)

-2 -1 0 1 2-1

0

1

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4

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Ra

ng

e (

m)

Cross-Range (m)

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Simulation Rail-SAR Drone-SAR

23

Downward-Looking SAR

Radar Control

Drone Control

Point antennas at the ground in order to change imaging plane.

Wi-Fi

Raspberry Pi

Computer

Time Domain Radar

24

Preliminary Investigation

• Currently mounted on a vehicle for preliminary investigation.

• Attached drone-SAR system on extension pole and pointed downward.

• Drive across parking lot and collect range profiles.

• Strong ground bounce and minor residual platform

returns.

• Height information is captured.

• Multiple scattering is visible.

25

Resulting Range Profiles R

an

ge

(m

)

Time (s)

0 20 40 600

0.5

1

1.5

2

2.5

3

3.5

4

-30

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-5

0

Person Car 3 Car 2 Car 1 Car 2 Car 1

• Strong ground bounce and minor residual platform

returns.

• Height information is captured.

• Multiple scattering is visible.

26

Resulting Range Profiles R

an

ge

(m

)

Time (s)

0 20 40 600

0.5

1

1.5

2

2.5

3

3.5

4

-30

-25

-20

-15

-10

-5

0

Person Car 3 Car 2 Car 1 Car 2 Car 1

Car 3 Car 2

• Strong ground bounce and minor residual platform

returns.

• Height information is captured.

• Multiple scattering is visible.

27

Resulting Range Profiles R

an

ge

(m

)

Time (s)

0 20 40 600

0.5

1

1.5

2

2.5

3

3.5

4

-30

-25

-20

-15

-10

-5

0

Person Car 3 Car 2 Car 1 Car 2 Car 1

Car 3 Car 2

Person Car 3

• Devised and demonstrated a portable, lightweight SAR system that can be mounted on a small drone.

• Pros:

+ Low-cost and portability opens up many new possibilities for in-situ measurements that were prohibitive in the past.

• Cons:

– Needs prominent scatterer (absence of navigation data).

– Drone flight instability.

28

Conclusion

• Continue downward-looking SAR for frontal view imaging of targets.

• Examine radar signatures under co-polarized and cross-polarized scenarios.

• SAR imagery of targets through optical obstructions (smoke, foliage).

• Blind motion compensation / obtain navigation data.

29

Future Work

Thanks to Sam Grayson, Tiffany Dang, Li Wang, Reid Li, and Kevin Chaloupka for their assistance.

30

Acknowledgments