main thesis.pptx

1

Welcome To Our Presentation

Presented By:

S. M. M. Hossain MahmudStudent ID:090924ECE DisciplineKhulna UniversityKhulna-9208.

A. K. M. Tohidur RahmanStudent ID:090918ECE DisciplineKhulna UniversityKhulna-9208.

Tapan Kumar BiswasStudent ID:090933ECE DisciplineKhulna UniversityKhulna-9208.

Electronics and Communication Engineering Discipline Khulna University Khulna-9208.

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Supervised By:Shakila NazninLecturerECE DisciplineKhulna UniversityKhulna-9208.

External Member:Md. Abdul AlimAssistant Professor ECE Discipline Khulna University Khulna-9208.

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Thesis Title

Analysis of Target Detection Performance and Reduction of Interrupting Signals at the Receiver of Coherent MIMO Radar Using Space Time Adaptive

Processing

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Outline

Introduction

MIMO Radar

Types of MIMO Radar

Detection performance Analysis of Coherent MIMO Radar

Space Time Adaptive Processing (STAP)

STAP Architecture

STAP Mechanism

Reduction of interrupting signals

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Introduction

Radar: Radio detection and Ranging.

It radiates energy into space and detect echoes reflected from target.

What is Radar?

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Radar Function

Detection

Determining the received signal is an echo return from target.

Directly related to SNR at the receiver end.

Parameter estimation

Range

Velocity

Angle

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What is MIMO Radar?

MIMO Radar

A radar system that employs multiple transmit waveforms and has the

ability to simultaneously process signals received at multiple antennas.

Every antenna element transmits different waveforms.

Matched filtering used at receiver for waveforms diversity.

Antenna elements of MIMO radar can be co-located or distributed.

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Target

Rx

Tx

MIMO Radar System

Fig. 1: MIMO Radar system

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Types of MIMO Radar

Statistical MIMO Radar:

Has often widely spaced apertures.

The target response for each transmitter-receiver pair is statistically

independent.

Its applicable to different look angles or different frequencies.

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Coherent MIMO radar

Closely spaced apertures

Operating on the same frequency

Same target response to all transmitter-receiver pairs

Target localization by coherent MIMO radar offers high resolution

Types of MIMO Radar

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Why MIMO Radar?

Reduce coherent energy on target

Supports multiple sensors

Offers localization with high accuracy.

Handling of multiple targets

Improved Doppler processing through diversity of look angles

Mitigation of the problem of low radial velocities

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-20 -15 -10 -5 0 5 10 15 20 250

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1probability of detection for coherent MIMO radar

SNR(dB)

prob

abili

ty o

f de

tect

ion

Coherent MIMO,M=1, N=5

Coherent MIMO,M=5, N=5Coherent MIMO,M=9, N=5

Fig. 2: Probability of Detection Plotted vs SNR for Coherent MIMO Radar for variable M.

Probability of Detection for Variable M

The equation for is

Depends on Number of receiving

antennas SNR

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-20 -15 -10 -5 0 5 10 15 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1probability of detection for coherent MIMO radar

SNR(dB)

pro

babili

ty o

f dete

ction


Coherent MIMO,M=5, N=5Coherent MIMO,M=5, N=9


The equation for is

Fig. 3: Probability of Detection Plotted vs SNR for Coherent MIMO Radar for variable N

Probability of Detection for Variable N

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-20 -15 -10 -5 0 5 10 15 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1probability of detection for coherent MIMO radar with STC waveforms

SNR(dB)

prob

abili

ty o

f de

tect

ion

STC-MIMO,M=1, N=5

STC-MIMO,M=4, N=5STC-MIMO,M=9, N=5

Probability of Detection with STC for Variable M

The equation for is

Fig. 4: Probability of Detection Plotted vs SNR for STC Coherent MIMO Radar for variable M

Depends on Numbers of transmitting

pulses Numbers of receiving antenna SNR

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-20 -15 -10 -5 0 5 10 15 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1probability of detection for coherent MIMO radar with STC waveforms

SNR(dB)

pro

babili

ty o

f dete

ction

STC-MIMO,M=5, N=1

STC-MIMO,M=5, N=5STC-MIMO,M=5, N=9

STC-MIMO,M=5, N=13

The equation for

Fig. 5: Probability of Detection Plotted vs SNR for STC Coherent MIMO Radar for variable N

Probability of Detection with STC for Variable N

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Comparison of probability of detection between with and without STC

Fig. 6: Comparison between Probability of Detection Plotted vs SNR for with and without STC

-20 -15 -10 -5 0 5 10 15 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Comparison of probability detection

SNR(dB)

pro

bability o

f dete

ction

STC-MIMO,M=5, N=5

MIMO,M=5, N=5

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A. K. M. Tohidur Rahman

090918

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Space Time Adaptive Processing(STAP)

Space Time Adaptive Processing

Refers to the ability to simultaneously process spatial sensor and temporal input data

Offers clutter and jamming cancellation to detect moving targets.

Its basically an adaptive filter, which can filter over the spatial and temporal (or time) domain.

The goal of STAP:

It takes a hypothesis that there is a target at a given location and velocity

Its create a filter that has high gain for that specific location and velocity, and while applying proportional attenuation for all signals.

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2-D Interfering (clutter, jammer) signal locations are not precisely defined

Required rejection (side lobe level) is not achievable with conventional

filtering in presence of system errors

Beam broadening that results from uniformly lowering side lobes with

heavy tapers is not needed

Improved minimum detectable velocity and angle coverage close to

jamming

Why Space-Time-Adaptive-Processing?

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Adaptive Array Processing

Fig. 7: A linear adaptive array

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} N antennas

} M pulses

} NM weights (degrees of

freedom)

Z

T TT T TT T TT

w11 w1M wN1 wNM

STAP output = WY

STAP weightmatrix

Signal Outputw = R–1S R = covariance matrix

S = steering vector

Optimum weights

...

Space-Time Adaptive Processing

Fig. 8: Optimize Space-Time Adaptive Processing

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Dimensionality can be very large: NM can be 102 to >104

Covariance matrix unknown a priori and must be estimated from the radar

data

Large search space of interest

Space-Time Adaptive Processing (Contd…)

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Range gates

zz

z

Puls

es

T

T

T

Elements

T = pulse repetition intervalz = A/D sampling period

Radar data cube

Rangegate of interest

Estimate interferenceusing this data

(training region)

Range gatesz z z z

Fig. 9: Radar Data and Interference Estimation


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Space Time Steering matrix:

A Kronecker product (an operation on two matrices of arbitrary size resulting in a block matrix ) of temporal steering matrix and spatial steering matrix

Temporal steering matrix is the DFT of frequency resolution

Spatial steering matrix represents the extent of correlation between each active pixel to its neighboring

1 … N

TR

[ 1; ej2πω; ej2πω·2; …ej2πω·(N-1)]


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Covariance Matrix

It represents the degree of correlation across both antenna array inputs

and over the pulses comprising the CPI (coherent processing interval).

It creates an optimal filter and remove undesired signals. The undesired

signals include noise, clutter and jammers.

Basically, the covariance matrix will be used to compute the optimal

filter, it also contain the target data.

R=+


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N Blocks

M B

locks𝑹=𝐸 {𝒀 𝒀𝑯 }=[ 𝑄11 𝑄12 … 𝑄1 𝑀

𝑄21 𝑄22 . 𝑄2𝑀

⋯ … ⋱ ⋮𝑄𝑁 1 𝑄𝑁 2 … 𝑄𝑁𝑀

] An matrix . An block matrix with

block size

Space-Time Covariance Matrices for Noise


𝑅𝑛=𝜎2 𝑰𝑁=𝜎2[ 𝑰 𝑁 0 … 00 𝑰 𝑁 . 0⋮ . ⋱ ⋮0 0 … 𝑰𝑁

]

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Jammer signals in different pulses are independent.

Jammer signals in different pulses are independent.

Jammer signals in different matched filter outputs are independent.

Jammer signals in different matched filter outputs are independent.

Block diagonalBlock diagonal

Numbers of jammersNumber of powersJammer spatial steering vector


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Separable: N+L tapsNon separable: NL taps

Joint processDoppler frequencies and angles

Joint processDoppler frequencies and angles

Independent process Doppler frequencies and angles

Independent process Doppler frequencies and angles

Angle processing

Doppler processingSpace-time

processing

L: # of radar pulses

L


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S. M. M. Hossain Mahmud090924

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EstimateInterferenceEstimate

Interference

ApplySTAP

Weights

ApplySTAP

Weights

Pre

proc

esso

rP

repr

oces

sorData cube

ComputeSTAP

Weights

ComputeSteering Vectors

ComputeSteering Vectors

Det

ectio

nsReduced dimensionspace

Beam Angle &Target Doppler

Selection

Beam Angle &Target Doppler

Selection

W=

Fig. 10: Generic STAP Architecture


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• Preprocessing may involve beamforming and Doppler filtering• Rejection of some interference nonadaptively• Adapt on small number of preprocessor outputs


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x y

OutputInput

A matched filter Colored noise Joint space (array beamforming) and time (Doppler)

)(

Matched Filter


Z=WY

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PulseE

lem

ent

Doppler bin

Ele

men

tDopplerfiltering

Pulse

Bea

m

Doppler bin

Bea

m

Dopplerfiltering

Spatialfiltering

Spatialfiltering

Element-SpacePre-Doppler

Element-SpacePost-Doppler

Beam-SpacePre-Doppler

Beam-SpacePost-Doppler

STAP algorithms classified by domain in which adaptivity occurs There are performance differences between algorithms

Taxonomy of STAP Architectures


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AdaptiveBeamforming

clutterNulling

AdaptiveBeamforming

clutterNulling

Beamspace

STAP

jammerNulling

Beamspace

STAP

jammerNulling

clutterTrainingclutter

TrainingjammingTrainingjammingTraining

N ElementsM Pulses

B BeamsM Pulses

Step 1 Step 2Detection

andMetrics

Detectionand

Metrics

• Requires training data free of main lobe clutter for Step 1– Beyond the horizon range gates in low PRF– Doppler filter away from mainlobe clutter

• Beamspace pre- or post-Doppler STAP clutter nulling


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Besides providing adequate power aperture product, the radar system design

must incorporate:

- A mechanism to suppress clutter returns

- Jammer suppression capability

Collectively refers to clutter and jamming signals as interference

Detection performance depends on the signal-to-interference-plus-noise ratio

(SINR) and specified false alarm rate

SINR=SNR

Detection Phenomenon


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Two-dimensional filtering required to cancel interference

Target Jamming

GroundClutter

v

–1

0

1

sin (Azimuth)

0

Doppler(H

z)0

10203040

SNR

(dB

)


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The total received signal contains returns from the target, clutter and jammer combined.

The signal is a data cube with three dimensions (range bins x number of elements x number of pulses).


Parameters assumed CNR: 30dB SNR: 10dB, JSR: 0dB

Space-time beampattern is the antenna gain as a function of angle of arrival and Doppler frequency.

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sine angle

norm

aliz

ed d

oppl

erTotal Return spectrum before STAP Detection of target, clutter, noise & jammer

-1 -0.5 0 0.5 1-1

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0

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1

42

44

46

48

50

52

54

56

58

60

Fig. 11: Total return spectrum before STAP detection.

Total Return Spectrum at Receiver

Target

Clutter+Noise

Jammer

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-1-0.5

00.5

1

-1

-0.5

0

0.5

140

45

50

55

60

65

sine angle

Total Return spectrum before STAP Detection of target, clutter, noise & jammer

normalized doppler

Total Return Spectrum at Receiver (Contd…)

Fig. 12: 3-D plot of total return spectrum at the receiver end with target, clutter, noise and jammer.

Target Clutter+Noise

Jammer

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sine angle

norm

alized d

opple

rSTAP Detection of target & jammer; clutter removed

-1 -0.5 0 0.5 1-1

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0

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1

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0

Clutter Removed by STAP

Fig. 13: STAP detection; Removal of clutter and noise while target & jammer remains.

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00.5

1

-1

-0.5

0

0.5

1-60

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0

sine angle

STAP Detection of target & jammer; clutter removed

normalized doppler

Fig. 14: 3-D plot of STAP detection; Removal of clutter and noise while target & jammer remains.

Clutter removed by STAP (Contd…)

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sine angle

norm

alized d

opple

rSTAP Detection of target; jammer & clutter removed

-1 -0.5 0 0.5 1-1

-0.8

-0.6

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0

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1

-60

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0

Jammer and clutter removed by STAP

Fig. 15: Output of STAP processor; Removal of jammer and clutter while only target remains.

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-1-0.5

00.5

1

-1

-0.5

0

0.5

1-60

-50

-40

-30

-20

-10

0

sine angle

SNR after STAP Detection of target, clutter, noise & jammer

normalized doppler

Fig.16: 3-D plot of output of STAP processor; Removal of jammer and clutter while only target remains.

Jammer and clutter remove by STAP (Contd…)

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References

1. M. Skolnik, “Introduction To Radar Systems”, 3rd ed. McGraw-Hill, 2001.2. ŞAFAK BİLGİ AKDEMİR, “AN OVERVIEW OF DETECTION IN MIMO RADAR”

published in SEPTEMBER 2010.3. J. Li and P. Stoica, “MIMO radar with colocated antennas,” IEEE Signal Processing Magaz.,

vol. 24, no. 5, pp. 106–114, Sept. 2007.4. Chaoran Du, “Performance Evaluation and Waveform Design for MIMO Radar”, The

University of Edinburgh, March 2010.5. Mark A. Richards, “Fundamentals of Radar Signal Processing”, Georgia Institute of Technology,

McGraw-Hill-2005.6. P. Tait, “Introduction to Radar Target Recognition”, Radar, Sonar and Navigation Series 18,

edited in 2009, The Institution of Engineering and Technology, London. 7. Z. C. Yang*, X. Li, and H. Q. Wang, “SPACE-TIME ADAPTIVE PROCESSING BASED ON

WEIGHTED REGULARIZED SPARSE RECOVERY”, Electronics Science and Engineering School, National University of Defense Technology, Changsha 410073, China.

8. Janice Onanian McMahon, “Space-Time Adaptive Processing on the Mesh Synchronous Processor”, THE LINCOLN LABORATORY JOURNAL, VOLUME 9, NUMBER 2, 1996.

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References9. J. R. Guerci, “Space-Time Adaptive Processing for Radar”, 2003 ARTECH HOUSE, INC.10. Dr. Marshall Greenspan, “MIMO Radar Signal Processing of Space Time Coded Waveforms”,

IEEE Signal Processing Society Baltimore Chapter MeetingMay 21, 2008. 11. Janice Onanian McMahon, “Space-Time Adaptive Processing on the Mesh Synchronous

Processor”, THE LINCOLN LABORATORY JOURNAL, VOLUME 9, NUMBER 2, 1996.12. Brian R. Hunt, Ronald L. Lipsman, Jonathan M. Rosenberg “A Guide to MATLAB for Beginners

and Experienced Users”, Second Edition, cambridge university press-2006.13. Mahafza, B. R., “Radar Signal Analysis and Signal Processing Using MATLAB”, Chapman and

Hall/CRC, Boca Raton, FL, 2008.14. Bassem R. Mahafza, Ph.D., Atef Z. Elsherbeni, “MATLAB Simulations for Radar Systems

Design”, 2004 by Chapman & Hall/CRC CRC Press LLC15. Bassem R. Mahafza, Ph.D., “Radar Systems Analysis and Design Using MATLAB”, 2008 by

Chapman & Hall/CRC.

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THANKS TO ALL

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