ASU MAT 591: Opportunities In Industry!
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ASU MAT 591: Opportunities In Industry!. Advanced MTI Algorithms. Howard Mendelson Principal Investigator 21 August 2000. Problem Advanced MTI Algorithms.

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Asu mat 591 opportunities in industry

ASU MAT 591: Opportunities In Industry!


Asu mat 591 opportunities in industry

Advanced MTI

Algorithms

Howard Mendelson

Principal Investigator

21 August 2000


Problem advanced mti algorithms

ProblemAdvanced MTI Algorithms

  • SAR systems provide excellent intelligence concerning status of fixed installations (assuming no electronic countermeasures (ECM) are employed)

  • Warfighter requires precise information describing MOVING formations of troops and weapons

    • Formations may be slow moving and thus difficult to distinguish from background clutter

    • Formations (as well as fixed targets) may be screened by ECM

  • Our customers now specify high fidelity moving target indication (MTI) and fixed target indication (FTI) with interference rejection capabilities for their battlefield surveillance systems.

  • These issues make it imperative for us to develop the techniques necessary to provide these capabilities


State of the art advanced mti algorithms

STATE OF THE ARTAdvanced MTI Algorithms

  • DPCA

    • Not data adaptive

  • ADSAR

    • Data adaptive but not jammer resistant

  • SPACE TIME ADAPTIVE PROCESSING (STAP)

    • No Fielded GMTI Systems

    • Computationally Intensive

    • Traditional SMI Approach Produces Large Numbers of False Alarms


Approach advanced mti algorithms

ApproachAdvanced MTI Algorithms

  • Develop Post Doppler Eigenspace Analysis Techniques

    • Advantages

      • Lower false alarm rate than traditional SMI approach

      • Simultaneous SAR and MTI in the presence of ECM

      • Common processing framework for clutter and jammer suppression

      • Higher Signal-to-Background Ratio (SBR) after interference suppression

      • Smaller training data set required for STAP algorithms

      • Computational Efficiency


Advanced mti algorithms

Advanced MTI Algorithms

Sample Matrix Inversion (SMI)

Interference Suppression Algorithm

Input Data (N channels)

Invert Covariance

Matrix

Apply

Inverse

Form Covariance Estimates

Detection

Processing

Beamform


Advanced mti algorithms1

Eigendecomposition

Interference SuppressionAlgorithm

Advanced MTI Algorithms

Input Data (N channels)

Perform

Eigendecomposition

Determine No. of

Interference Sources

Form Covariance Estimates

Project Data Orthogonally

to Interference Subspace

Detection

Processing

Beamform


Covariance estimation

Covariance Estimation

Channel 1

N/2 Rng Cells

Guard

Cells

Cell of

Interest

Guard

Cells

N/2 Rng Cells

Channel 2

N/2 Rng Cells

Guard

Cells

Cell of

Interest

Guard

Cells

N/2 Rng Cells

X1

.

.

.

XN

Channel N

N/2 Rng Cells

Guard

Cells

Cell of

Interest

Guard

Cells

N/2 Rng Cells

No. of range cells used for Eigen

processing is typically

1.5 x No.of channels

(Higher for SMI)

Covariance estimate is computed

in sliding window at every pixel

No. of guard cells depends on range

resolution

His complex conjugate transpose


Weight calculation smi

Weight Calculation (SMI)

Sample Matrix Inversion (SMI)

subject to

$

R

Sample Covariance Matrix

C

Constraint Matrix

f

Coefficient Vector

w

Weight

Vector

Hermitian adjoint (conjugate transpose)

H


Weight calculation mne

subject to

and

Matrix of eigenvectors of estimated covariance matrix

associated with interference

Q

r

C

Constraint Matrix

f

Coefficient Vector

w

Weight Vector

Weight Calculation (MNE)

Minimum Norm Eigencancler (MNE)


Lm m ds isrs ir d sar testbed

LM M&DS – ISRSIR&D SAR Testbed

24”

7”

adjustable

Channel 2

Receive

Channel 1

Transmit/Receive

Channel 0

Receive

flight


Controlled mover in clutter eigendecomposition advanced mti algorithms

Controlled Mover in Clutter (Eigendecomposition)Advanced MTI Algorithms

Controlled Moving Target


Controlled mover in clutter smi advanced mti algorithms

Controlled Mover in Clutter (SMI)Advanced MTI Algorithms


Pri stagger algorithm advanced mti algorithms

PRI Stagger AlgorithmAdvanced MTI Algorithms

FFT

1 2 3 . . . P - 1 P

FFT

S

T

A

P

FFT

1 2 3 . . . P - 1 P

Elements (or beams)

FFT

FFT

1 2 3 . . . P - 1 P

FFT


Covariance estimation1

Covariance Estimation

Channel 1 Stagger 0

N/2 Rng Cells

Guard

Cells

Cell of

Interest

Guard

Cells

N/2 Rng Cells

Channel 2 Stagger 0

N/2 Rng Cells

Guard

Cells

Cell of

Interest

Guard

Cells

N/2 Rng Cells

X10n

.

.

.

XLNstg-1n

Channel L Stagger Nstg - 1

N/2 Rng Cells

Guard

Cells

Cell of

Interest

Guard

Cells

N/2 Rng Cells

No. of range cells used for Eigen

processing is typically

1.5 x No.of channels x No. of staggers

(Higher for SMI)

Covariance estimate is computed

in sliding window at every pixel

No. of guard cells depends on range

resolution

His complex conjugate transpose


Data collect radar image tactical targets

Data Collect Radar Image Tactical Targets


Data collect tactical targets

Unprocessed Image

SMI Processing

Eigendecomposition Processing

Data Collect Tactical Targets


Cfar detectors gmti

CFAR DETECTORS(GMTI)

Adaptive Matched

Filter (SMI)

H1

>

<

H2

aAMF

H1

>

<

H2

Generalized Likelihood

Ratio Test (SMI)

aGLRT

H1

>

<

H2

Eigendecompsition Likelihood

Ratio Test

aPC


Detection performance p fa 10 6

Detection Performance (Pfa = 10-6 )

Unprocessed Image

SMI - AMF Detection Reports

SMI - GLRT Detection Reports

LRT - Eigendecomposition Detection Reports


Detection performance p fa 10 61

Unprocessed Image

SMI - AMF Detection Reports

SMI - GLRT Detection Reports

LRT - Eigendecomposition Detection Reports

Detection Performance Pfa = 10-6


Relocation algorithm

RELOCATION ALGORITHM

  • Uses Channel-to-Channel Phase Differences to Obtain Target Direction of Arrival (DOA)

  • Originally Developed for Three Channel “Uniformly” Spaced Array Without PRI Stagger

  • Assumed Clutter as only Interference Source

    • Insufficient number of degrees of freedom available to deal with more than one interfering source

  • Can be extended

    • No. of channels greater than 3

    • Multiple interfering sources

    • Non-uniform spacing


Relocation algorithm1

RELOCATION ALGORITHM

Assumed Signal Model


Relocation algorithm2

r

r

y

(

e

,

s

)

(

e

,

s

)

$

$

=

@

1

1

1

Tgt

r

r

y

(

e

,

s

)

(

e

,

s

)

$

$

=

@

2

2

2

Tgt

=

e

First ei

genvector

orthoganal

to clutte

r directio

n

$

1

=

e

Second e

igenvector

orthogana

l to clutt

er directi

on

$

2

Same eigen

vectors co

mputed dur

ing interf

erence sup

pression

and detect

ion proces

sing

RELOCATION ALGORITHM

Phase of target vector can now be found

by solving for roots of quadratic

Solution which provides largest return

after beamforming is assumed correct


Relocation algorithm example

Relocation Algorithm - Example

Relocated Targets

Original Target Detections


Relocation algorithm 2

RELOCATION ALGORITHM - 2

Assumed Signal Model

Complex images from each channel are assumed

to have been relocated to a common point


Relocation algorithm 2 cont

r

r

y

(

e

,

s

)

(

e

,

s

)

$

$

=

@

1

1

1

Tgt

r

r

y

(

e

,

s

)

(

e

,

s

)

$

$

=

@

2

2

2

Tgt

=

e

First ei

genvector

orthoganal

to clutte

r directio

n

$

1

=

e

Second e

igenvector

orthogana

l to clutt

er directi

on

$

2

Same eigen

vectors co

mputed dur

ing interf

erence sup

pression

and detect

ion proces

sing

RELOCATION ALGORITHM - 2 (cont.)

Phase of target vector can now be found

by solving for roots of quadratic

Solution which provides largest return

after beamforming is assumed correct


Geolocation accuracy

Geolocation Accuracy

Cramer Rao bound of interferometer measurement accuracy

used to estimate cross range error


Target reports

Target Reports

Known Targets

SMI based STAP

Eigenanalysis based STAP


Target reports1

Target Reports

Original Detections

Relocated Targets

Unprocessed Target Detections

Relocated Target Detections


Multi stage false alarm reduction processing

Multi-Stage False Alarm Reduction Processing

Covariance

Estimate

Multichannel

Complex

Image Data

Find Eigenvalues

and Eigenvectors

Find Noise

Subspace

Dimension

Form Interference

Suppression

Projections

Form Estimated

Steering Vector

Produce

Interference

Suppressed

Data Field

Form Image

Projections

Compute AOA

(Radial Speed)

Estimates

Produce Low

Resolution SAR

Image

Perform CFAR

Thresholding

Compute

Cancellation

Ratios of

Threshold

Crossings

Determine AOA

Consistency

of Estimates

of Possible

Detections

Detection reports

Location, Speed

and Heading Estimates


Summary

SUMMARY

  • Multiple post-Doppler STAP algorithms studied and evaluated for clutter suppression and target detection

    • Eigenanalysis, SMI

    • Single Doppler bin, adjacent Doppler bin, PRI stagger

  • “Mono-pulse” location algorithm developed and tested on collected data

  • Work ongoing to develop algorithm upgrades


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