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IGARSS 2011 . PARALLEL FREQUENCY RADAR VIA COMPRESSIVE SENSING. You Yanan , Li Chunsheng , Yu Ze. BEIHANG UNIVERSITY 201 LAB. OUTLINE. 1. INTRODUCTION. 2. background of compressive sensing. 3. PARALLEL FREQUENCY RADAR BASED ON COMPRESSIVE SENSING. 4. simulation results.

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slide1

IGARSS 2011

PARALLEL FREQUENCY RADAR

VIA COMPRESSIVE SENSING

  • You Yanan, Li Chunsheng, Yu Ze

BEIHANG UNIVERSITY 201 LAB

slide2

OUTLINE

1. INTRODUCTION

2. background of compressive sensing

3. PARALLEL FREQUENCY RADAR BASED ON

COMPRESSIVE SENSING

4. simulation results

5. CONCLUSION

slide3

1. INTRODUCTION

  • Traditional radar utilizes Shannon-Nyquist theorem for high bandwidth signal sampling, which induces the complicated system.
  • Compressive sensing (CS) indicates that the compressible signal using a few measurements can be reconstructed by solving a convex optimization problem.
slide4

1. INTRODUCTION

  • The structure of compressive sensing radar with frequencies transmitted in parallel is firstly presented in On compressive sensing applied to radar
  • Parallel frequency radar theoretically cannot degrade the resolution compared with a traditional radar system and effectively reduces the sampling rate.
slide5

2. BACKGROUND OF COMPRESSIVE SENSING

sparse scene S

convex problem

  • Basis Pursuit (BP) , Matching Pursuit (MP) and Orthogonal Matching
  • Pursuit (OMP) can solve this convex problem
  • Sparse scene S can be reconstructed by the above algorithms.
slide6

3. PARALLEL FREQUENCY RADAR BASED ON

  • COMPRESSIVE SENSING

3.1 Observation mode

  • The radar platform moves along track (azimuth) direction with constant velocity.
  • two-dimension observation scene is limited with the along track (azimuth) direction and the cross track (range) direction.
  • Parallel frequency radar synchronously transmits M single frequency pulses in each azimuth and then received the phase shift echoes.
slide7

3. PARALLEL FREQUENCY RADAR BASED ON

  • COMPRESSIVE SENSING

3.2 Resolutionand sampling rates

Ground observation grid

Azimuth

Range

  • Range resolution of traditional pulse compressive radar
  • Parallel frequency radar synchronously transmits M single frequency pulses in each azimuth with the constant .
  • Range resolution of parallel frequency radar via compressive sensing

Conclusion :If we set the appropriate M and

slide8

3. PARALLEL FREQUENCY RADAR BASED ON

  • COMPRESSIVE SENSING

3.2 Resolutionand sampling rates

Conclusion:If we set the appropriate M and D

The sampling rate of parellel frequency radar is M/T complex samples per second. Define the time-bandwidth product to be D=BT, B is the total bandwidth of M single frequency pulses. Then, the sampling rate is shown as BM/D.

BM/D

B

Traditional radar system

Parallel frequency radar system

slide9

3. PARALLEL FREQUENCY RADAR BASED ON

  • COMPRESSIVE SENSING

3.3 Imaging based on compressive sensing

  • The phases of echo signal contain the information of the targets.

The kth echo in a certain azimuth is

The phase information of the target is

The discrete representation is

Yis identity matrix

k a estimation

3. PARALLEL FREQUENCY RADAR BASED ON

  • COMPRESSIVE SENSING
Ka estimation
  • The targets will be focused on the respective zero Doppler by Ka.
  • Ka is calculated precisely before compressing the azimuth phases.
  • Ka varies with the range cells and the signal frequencies.
f construction

3. PARALLEL FREQUENCY RADAR BASED ON

  • COMPRESSIVE SENSING
F construction

1D observation grid

Mechoes

phase

detector

Msingle frequency pulses

M frequencies

3D prior F

PriorF

Measurement matrix is constructed by prior phase information using discrete grids.

observation scene reconstruction

3. PARALLEL FREQUENCY RADAR BASED ON

  • COMPRESSIVE SENSING
Observation scene reconstruction

Observation scene

Observation grids

Mechoes

phase

detector

M frequencies

Msingle frequency pulses

Na×Nr

PriorF

recover

1×Nr

OMP

M×Nr

M frequencies

illation

3. PARALLEL FREQUENCY RADAR BASED ON

  • COMPRESSIVE SENSING
Illation

Echo equation 1

Instantaneous range

Approximate representation

Echo equation 2

Demodulating and detecting the phase shift

slide14

4. simulation results

The parameters in simulations: M = 30, △f= 5MHz, RMAX = 20km andN = 512.

Azimuth cross section(amplitude)

Imaging result of point target of the parellel frequency radar via compressive sensing

Range cross section(amplitude)

slide15

4. simulation results

Imaging results of pulse compression radar and parallel frequency radar via compressive sensing with single or several point targets

Range

Range

Compressive sensing algorithm of parallel frequency radar

RD algorithm of pulse compression radar

Parallel frequency radar via compressive sensing

Pulse compression radar

slide16

4. simulation results

Imaging results of traditional SAR and parallel frequency radar via compressive sensing with the same data amount

Parallel frequency radar via compressive sensing

Traditional SAR

slide17

Amplitude

Amplitude

Range

Azimuth

Azimuth

Range

Azimuth

Range

4. simulation results

Parallel frequency radar via compressive sensing

Pulse compression radar

Azimuth

Range

Conclusion: Sampling rate is reduced by narrow-bandwidth of single frequency pulse. The sparse scene is reconstructed using 10% original data. Side lobes of the range disappear via compressive sensing.

slide18

4. simulation results

The ability of distinguishing the adjacent targets in the range direction

Pulse compression radar

Parallel frequency radar via compressive sensing

slide19

5. CONCLUSION

  • This presentation introduces parallel frequency radar and imaging approach based on compressive sensing.
  • The novel radar can reduce data rate and maintain range resolution with the premise of the appropriate parameters.
  • The measurement matrix F is composed of the priori phases. The observation scene is reconstructed by OMP.
slide20

5. CONCLUSION

  • The imaging approach based on compressive sensing avoids side lobes of the range.
  • The noise in received signal is not taken into account in the simulations, it is not ignored in practice.
  • More researches are required on the capability resisting the noise interference of the signal processing based on compressive sensing in the future.
slide21

IGARSS 2011

Thank you for your attention!

BEIHANG UNIVERSITY 201 LAB