Adaptive Beamforming Techniques for Sidelobe Control ??2012-10-11ABF-SCJM JDG 12/19/2005 MIT Lincoln...

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Transcript of Adaptive Beamforming Techniques for Sidelobe Control ??2012-10-11ABF-SCJM JDG 12/19/2005 MIT Lincoln...

  • ABF-SCJMJDG 12/19/2005

    MIT Lincoln Laboratory

    Adaptive Beamforming Techniques for Sidelobe Control and Mitigation of

    Nonstationary Interference

    JAMJAM

    Jacob D. GriesbachGerald Benitz

    MIT Lincoln Laboratory

    June 7th, 2005This work is sponsored by the Air Force, under Air Force contract FA8721-05-C-0002. Opinions, interpretations, conclusions and

    recommendations are those of the authors, and are not necessarily endorsed by the United States Government.

  • MIT Lincoln LaboratoryABF-SCJM 2 of 25

    JDG 12/19/2005

    Adaptive Beamforming Motivation

    Adaptive Beamforming (ABF) suppresses interference to improve SINR

    Low sidelobe beams benefit clutter suppression techniques and require fewer ABF DOFs to mitigate sidelobe jamming

    Allow nulling to track inter-CPI interference motion

  • MIT Lincoln LaboratoryABF-SCJM 3 of 25

    JDG 12/19/2005

    Lincoln Multi-Mission ISR Testbed (LiMIT)

    System Parametersfor GMTI Mode

    System Parametersfor GMTI Mode

    9.72 GHz180 MHz2,000 Hz56 ms848 cm18 cm

    Center Freq.BandwidthPRFCPIRx SubarraysHoriz. ApertureVert. Aperture

    Boeing 707

    Ft. Huachuca, AZN

    8 km

    25 km

    NAimpoint

    Aircraft

    Noise Jammer20-30 dB JNR

  • MIT Lincoln LaboratoryABF-SCJM 4 of 25

    JDG 12/19/2005

    LiMIT GMTI Processing

    Receiver /Front-End

    8 Receive-Only PRIs provide ABF training data before and after CPI

    LiMIT-tuned 2-Parameter Power-Variable-Training STAP algorithm1 LiMIT aperture transmits with a uniform taper that results in multiple Doppler-

    wrapped clutter ridges STAP algorithm uses phase to select training samples from modeled clutter ridge Will not cancel residual interference left over from ABF

    Adaptive beamforming goals Must suppress unwanted interference Low sidelobe beams from ABF help STAP suppress secondary clutter ridges Must also form a beamset that covers clutter to be mitigated by STAP

    CFARDetect

    DopplerProcessing STAP

    (Adaptive)Beamforming

    Param.Estimate Cluster Track

    RO ROTransmit / Receive Data (96 PRIs)

    8 Receive-Only PRIs 8 Receive-Only PRIs

    1G. Benitz, J.D. Griesbach, C. Rader, Two-Parameter Power-Variable Training STAP, Proceedings of the 38thAsilomar conference on signals, systems, and computers, Pacific Grove, CA, Nov. 7-10, 2004, pp. 2359-2363

  • MIT Lincoln LaboratoryABF-SCJM 5 of 25

    JDG 12/19/2005

    Outline

    Colored Noise Loading for Low Sidelobes

    Constrained DBU for stable tracking of jammer motion

    Data Results

    Conclusion

  • MIT Lincoln LaboratoryABF-SCJM 6 of 25

    JDG 12/19/2005

    Low Sidelobe BeamformingC

    onve

    ntio

    nal

    Bea

    mfo

    rmin

    g(C

    BF)

    Hv xChannelData (x)

    SteeringVector (v)

    OutputBeam Data

    CB

    Fw

    ith S

    V ta

    per

    Hv DxChannelData (x)

    SteeringVector (v)

    OutputBeam Data

    DvTaper ( )H=D D

    CBF optimally maximizes SNR to a given v Sidelobes are controlled (not data adaptive) Does not necessarily suppress strong or mainbeam interference sources

  • MIT Lincoln LaboratoryABF-SCJM 7 of 25

    JDG 12/19/2005

    Low Sidelobe Adaptive BeamformingA

    dapt

    ive

    Bea

    mfo

    rmin

    g(A

    BF)

    1H v R xChannelData (x)

    SteeringVector (v)

    OutputBeam Data

    AB

    Fw

    ith S

    V ta

    per

    1H v DR xChannelData (x)

    SteeringVector (v)

    OutputBeam Data

    DvTaper

    ABF optimally maximizes SINR to a given v Sidelobes are not necessarily controlled (data adaptive) Can suppress strong or mainbeam interference sources

  • MIT Lincoln LaboratoryABF-SCJM 8 of 25

    JDG 12/19/2005

    Colored Noise Loading

    Idea: Optimally suppress sidelobes+interference, by modeling external sidelobe interference in data covariance

    L

    clfclf

    Parameters:= Loading Level= Loading Frequencyclf

    L

    ( )1 2

    ( ) ( ) ( ) ( ) cl

    H H Hcl

    f

    L d = + v vR D v v v v D

    ( )diag=vD v

    1( )cl= +w R R v

    SteeringVector (v)

    1( )H cl+v R R xChannelData (x)

    OutputBeam Data

  • MIT Lincoln LaboratoryABF-SCJM 9 of 25

    JDG 12/19/2005

    Sidelobe Jamming Comparison

    ABF Tapered SV

    Using a tapered steering vector works with sidelobe jamming:

    Colored noise loading also works well with sidelobe jamming:

    ABF + CNL

  • MIT Lincoln LaboratoryABF-SCJM 10 of 25

    JDG 12/19/2005

    Mainbeam Jamming Comparison

    ABF Tapered SV

    TSV ABF does not appropriately model

    steering vector:

    Mainbeam jamming causes CNL ABF to trade-off jammer &

    sidelobe suppression:

    ABF + CNL

  • MIT Lincoln LaboratoryABF-SCJM 11 of 25

    JDG 12/19/2005

    ABF Colored Noise Loading

    1. Let u1- uk denote eigenvectors of R that have eigenvalues, 2 > Tev2. Let C denote linear constraints such that CHw = c

    =C v 1=c (MVDR constraint)3. Solve

    ( ) ( )( ) 11 1Hcl cl = + +w R R C C R R C c (Constrained LS)ABF + CNL

  • MIT Lincoln LaboratoryABF-SCJM 12 of 25

    JDG 12/19/2005

    Inequality Constrained ABFColored Noise Loading

    1. Let u1- uk denote eigenvectors of R that have eigenvalues, 2 > Tev2. Let C denote linear constraints such that CHw = c

    =C v 1=c (MVDR constraint)3. Solve

    ( ) ( )( ) 11 1Hcl cl = + +w R R C C R R C c (Constrained LS)

    2 2

    1 11T

    i j

    =

    c

    ?

    i j = C v u u

    The ABF now prioritizes the interference above sidelobes by ensuring the interference is adequately suppressed

    4. Check eigenvector inequality constraints

    [ ]1 2 21

    1 1T

    Hk

    k

    <

    u u w

    5a. If all constraints are satisfied done5b. If not add unmet constraints to constraint matrix

    6. Go to step 3

    Constrained ABF + CNL

  • MIT Lincoln LaboratoryABF-SCJM 13 of 25

    JDG 12/19/2005

    Outline

    Colored Noise Loading for Low Sidelobes

    Constrained DBU for stable tracking of jammer motion

    Data Results

    Conclusion

  • MIT Lincoln LaboratoryABF-SCJM 14 of 25

    JDG 12/19/2005

    Derivative Based Updating (DBU)

    DBU2 allows an ABF to track a spatially moving jammer Weight vector changes linearly in slow time

    where k denotes the relative pulse index throughout the CPI and n indexes fast-time (range)

    An augmented covariance matrix is computed

    An adaptive solution is formed for the center of the CPI

    DBU may also be applied in frequency for wideband jamming

    1 1k

    , , , ,2

    , , , , ,

    1 H Hk n k n k n k nH H

    k n k n k n k n k n

    kk kKN

    =

    x x x x

    Rx x x x

    1 =

    0w vRw 0

    Augmented steering vector with k = 0

    CPI center weight vector

    Weight vector derivative

    0

    2

    ,1 ,

    minH

    Hk k n

    k n=

    w vw xSolve suchthat 0k k= +w w w

    2S.D. Hayward, Adaptive beamforming for rapidly moving arrays, in CIE International Conference Proceedings, Oct. 1996, pp. 480--483

  • MIT Lincoln LaboratoryABF-SCJM 15 of 25

    JDG 12/19/2005

    DBU Effects(Example Simulation)

    Conventional ABF

    Spatially Moving Jammer

    DBU

    k = -1k = 0k = 1

    Inter-CPI Gain Variation

    Spatially Moving Jammer

  • MIT Lincoln LaboratoryABF-SCJM 16 of 25

    JDG 12/19/2005

    Constrained DBU

    Constrain DBU result to have constant gain throughout CPI Ensure unit gain on target (MVDR constraint)

    Ensure derivative is orthogonal to center weight vector(new constraint)

    Optimal solution now given by

    0 1H

    =

    w vw 0

    0 0H

    =

    w 0w v

    =

    v 0C

    0 v [ ]1 0T=c

    ( ) 10 1 1H =

    wR C C R C c

    w

    0k k= +w w w

    2

    ,1 ,

    minHk

    Hk k n

    k n=

    w vw x

  • MIT Lincoln LaboratoryABF-SCJM 17 of 25

    JDG 12/19/2005

    Constrained DBU Results

    Conventional DBU Constrained DBU

    k = -1k = 0k = 1

    Constraining the weight derivative to be orthogonal to the steering vector provides a gain invariant solution

    Holds gain fixed for steering vector direction May disrupt sidelobes

  • MIT Lincoln LaboratoryABF-SCJM 18 of 25

    JDG 12/19/2005

    Constrained DBU withColored Noise Loading

    Constrained DBU modifications for colored noise loading Add colored noise loading covariance to augmented covariance

    Add eigenvector inequality constraints to prioritize jammers over sidelobes

    Constrained DBU

    k = -1k = 0k = 1

    Constrained DBU w/ CNL

    2

    11

    1 1cl clk

    K

    k kK K

    =

    R R

    =

    v 0 uC

    0 v 211 0

    T

    =

    c

  • MIT Lincoln LaboratoryABF-SCJM 19 of 25

    JDG 12/19/2005

    Outline

    Colored Noise Loading for Low Sidelobes

    Constrained DBU for stable tracking of jammer motion

    Data Results

    Conclusion

  • MIT Lincoln LaboratoryABF-SCJM 20 of 25

    JDG 12/19/2005

    Ft. Huachuca GMTI Displays

    SAR Image (1m resolution)

    Range/Doppler DetectionRange/Doppler ClusterRange/Angle LocalizationGPS Ground TruthJammer Angle

    07/24/04 CPI# 98045687

  • MIT Lincoln LaboratoryABF-SCJM 21 of 25

    JDG 12/19/2005

    GMTI Movie

    Range/Doppler DetectionRange/Doppler ClusterRange/Angle LocalizationGPS Ground TruthJammer Angle

    Desired Beams Jamming Angles

    07/24/04 CPI# 9804568