High resolution sar imaging using random pulse timing
Download
1 / 24

High resolution SAR imaging using random pulse timing - PowerPoint PPT Presentation


  • 81 Views
  • Uploaded on

High resolution SAR imaging using random pulse timing. Dehong Liu. Joint work with Petros Boufounos.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' High resolution SAR imaging using random pulse timing' - rona


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
High resolution sar imaging using random pulse timing

High resolution SAR imaging using random pulse timing

Dehong Liu

Joint work with Petros Boufounos.

IGARSS’ 2011 Vancouver, CANADA


Outline
Outline

  • Overview of synthetic aperture radar (SAR)

  • Compressive sensing (CS) and random pulse timing

  • Iterative reconstruction algorithm

  • Imaging results with synthetic data

  • Conclusion and future work

2



Synthetic aperture radar sar
Synthetic Aperture Radar (SAR)

4

v

Reflection duration depends on range length.

azimuth

azimuth

Ground

Range


Strip map sar uniform pulsing
Strip-map SAR: uniform pulsing

5

v

azimuth

azimuth

Ground

Range


Data acquisition and image formation
Data acquisition and image formation

6

  • SAR acquisition follows linear model

    y= x, where

    y:Received Data,

    x:Ground reflectivity,

    :Acquisition function determined by SAR parameters, for example, pulse shape, PRF, SAR platform trajectory, etc.

  • Image formation: determine x given y and  .

    • Range Doppler Algorithm

    • Chirp Scaling Algorithm

      • Specific to Chirp Pulses


Sar imaging resolution

7

SAR imaging resolution

  • Range resolution

    • Determined by pulse frequency bandwidth

  • Azimuth resolution

    • Determined by Doppler bandwidth

    • Requiring high Pulse Repetition Frequency (PRF)

azimuth

Range


Trade off for uniform pulse timing

T

T

T

Reflection

Reflection

Reflection

overlapping

missing

8

Trade-off for uniform pulse timing

  • Tradeoff between azimuth resolution and range length

    • Reflection duration depends on range length

    • Increasing PRF reduces the range length we can image

    • High azimuth resolution means small range length.

Low azimuth resolution, large range.

T

T

Reflection

Reflection

High azimuth resolution, small range.

T

T

T

Reflection

Reflection

Reflection

High azimuth resolution, large range ?


Ground coverage at high prf

9

Ground coverage at high PRF

azimuth

range

  • Issue: missing data always in the same range interval

    • Produces black spots in the image

    • High resolution means small range coverage

  • Solution: Motivated by compressive sensing, we propose random pulse timing scheme for high azimuth resolution imaging.



Compressive sensing vs nyquist sampling

11

Compressive sensing vs. Nyquist sampling

  • Nyquist / Shannon sampling theory

    • Sample at twice the signal bandwidth

  • Compressive sensing

    • Sparse / compressible signal

    • Sub-Nyquist sampling rate

    • Reconstruct using the sparsity model


Compressive sensing and reconstruction

Φ

sparsesignal

measurements

12

Compressive sensing and reconstruction

  • CS measurement

  • Reconstruction

  • Signal model: Provides prior information; allows undersampling;

  • Randomness: Provides robustness/stability;

  • Non-linear reconstruction: Incorporates information through computation.

Φ

sparsesignal

measurements

Non-zeroes


Connection between cs and sar imaging
Connection between CS and SAR imaging

13

Question: Can we apply compressive sensing to SAR imaging?


Random pulse timing

Randomized timing

mixes missing data

14

Random pulse timing

Randomized pulsing interval

azimuth

range



Iterative reconstruction algorithm1

16

Iterative reconstruction algorithm

Note: Fast computation of  and H always speeds up the algorithm.


Efficient computation

17

Efficient computation 

Chirp Scaling Algorithm

y

Azimuth FFT

Fa

Chirp Scaling

(differential RCMC)

S-1

Range FFT

Fr

PrH

B-1

Bulk RCMC, RC, SRC

R-1

Range IFFT

Fr-1

Azimuth Compression/

Phase Correction

PaH

Computation of  follows reverse path

Computation as efficient as CSA

Azimuth IFFT

Fa-1



Experiment w synthetic data

19

Experiment w/ synthetic data

  • SAR parameters: RADARSAT-1

  • Ground reflectivity: Complex valued image of Vancouver area

  • Quasi-random pulsing: Oversample 6 times in azimuth, and randomly select half samples to transmit pulses, resulting 3 times effective azimuth oversampling.

  • Randomization ensures missing data well distributed


20

Radar data acquisition

Forward process

Ground

Radar

Raw Data

Radar

Image

Standard Algorithm

Classic Pulsing

low PRF

Image with low azimuth resolution

Iterative Algorithm

Simulated Ground Reflectivity

(high-resolution)

Random Pulsing

high PRF + missing data

Image with high azimuth resolution


Zoom in imaging results
Zoom-in imaging results

21

Uniform pulsing, Small PRF,

Small Doppler Bandwidth

True Ground Reflectivity

Random pulsing, High PRF,

Large Doppler Bandwidth


Zoom in imaging results1
Zoom-in imaging results

22

Uniform pulsing, Small PRF,

Small Doppler Bandwidth

True Ground Reflectivity

Random pulsing, High PRF,

Large Doppler Bandwidth



Conclusion

24

Conclusion

  • Proposed random pulse timing scheme with high average PRF for high resolution SAR imaging.

  • Utilized iterative non-linear CS reconstruction method to reconstruct SAR image.

  • Achieved high azimuth resolution imaging results without losing range coverage.

  • Noise and nadir echo interference issues.

  • Computational speed.

Future work


ad