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TRANSCRIPT
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ABF-SCJM
JDG 12/19/2005
MIT Lincoln Laboratory
Adaptive Beamforming Techniques for
Sidelobe Control and Mitigation ofNonstationary Interference
JAM
JAM
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.
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Adaptive Beamforming Motivation
Adaptive Beamforming (ABF) suppresses interference to improve SINR
Low sidelobe beams benefit clutter suppression techniques and requirefewer ABF DOFs to mitigate sidelobe jamming
Allow nulling to track inter-CPI interference motion
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Lincoln Multi-Mission ISR Testbed(LiMIT)
System Parametersfor GMTI Mode
System Parametersfor GMTI Mode
9.72 GHz
180 MHz
2,000 Hz
56 ms
8
48 cm
18 cm
Center Freq.
Bandwidth
PRF
CPI
Rx Subarrays
Horiz. Aperture
Vert. Aperture
Boeing 707
Ft. Huachuca, AZ
N
8 km
25 km
NAimpoint
Aircraft
Noise Jammer20-30 dB JNR
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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
Mustsuppress 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)
BeamformingParam.
EstimateCluster 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 38th
Asilomar conference on signals, systems, and computers, Pacific Grove, CA, Nov. 7-10, 2004, pp. 2359-2363
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Outline
Colored Noise Loading for Low Sidelobes
Constrained DBU for stable tracking of jammer motion
Data Results
Conclusion
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Low Sidelobe Beamforming
Conventional
B
eamforming
(CBF)
Hv x
Channel
Data (x)
Steering
Vector (v)
Output
Beam Data
CBF
withSVtap
er
Hv Dx
Channel
Data (x)
Steering
Vector (v)
Output
Beam Data
Dv
Taper
( )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
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Low Sidelobe Adaptive Beamforming
Adaptive
Be
amforming
(ABF)
1H v R x
Channel
Data (x)
Steering
Vector (v)
Output
Beam Data
ABF
withSVtap
er
1H v D R x
Channel
Data (x)
Steering
Vector (v)
Output
Beam Data
Dv
Taper
ABF optimally maximizes SINR to a given v
Sidelobes are not necessarily controlled (data adaptive)
Can suppress strong or mainbeam interference sources
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Colored Noise Loading
Idea: Optimally suppress sidelobes+interference, by modelingexternal sidelobe interference in data covariance
L
clf
clf
Parameters:
= Loading Level
= Loading Frequencyclf
L
( )1 2
( ) ( ) ( ) ( )
cl
H H H
cl
f
L d = + v vR D v v v v D
( )diag=vD v
1( )cl
= +w R R v
Steering
Vector (v)
1( )Hcl
+v R R xChannel
Data (x)
Output
Beam Data
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Sidelobe Jamming Comparison
ABF Tapered SV
Using a tapered steering
vector works with
sidelobe jamming:
Colored noise loading
also works well withsidelobe jamming:
ABF + CNL
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Mainbeam Jamming Comparison
ABF Tapered SV
TSV ABF does not
appropriately model
steering vector:
Mainbeam jamming
causes CNL ABF totrade-off jammer &
sidelobe suppression:
ABF + CNL
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ABF Colored Noise Loading
1. Let u1- u
kdenote eigenvectors ofR that have eigenvalues, 2 > T
ev2. Let C denote linear constraints such that CHw = c
=C v 1=c (MVDR constraint)3. Solve
( ) ( )( )
11 1H
cl cl
= + +w R R C C R R C c (Constrained LS)
ABF + CNL
Inequality Constrained ABF
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Inequality Constrained ABFColored Noise Loading
1. Let u1- u
kdenote eigenvectors ofR that have eigenvalues, 2 > T
ev2. Let C denote linear constraints such that CHw = c
=C v 1=c (MVDR constraint)3. Solve
( ) ( )( )
11 1H
cl cl
= + +w R R C C R R C c (Constrained LS)
2 2
1 11
T
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
H
k
k
<
u u w
5a. If all constraints are satisfied done
5b. If not add unmet constraints to constraint matrix
6. Go to step 3
Constrained ABF + CNL
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Outline
Colored Noise Loading for Low Sidelobes
Constrained DBU for stable tracking of jammer motion
Data Results
Conclusion
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Derivative Based Updating (DBU)
DBU2 allows an ABF to track a spatially moving jammer Weight vector changes linearly in slow time
where kdenotes the relative pulse index throughout the CPI andn 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 H
k 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 v
Rw 0
Augmented steering
vector with k= 0
CPI center weight vector
Weight vector derivative
0
2
,1
,
minH
H
k k n
k n=
w v
w xSolvesuch
that 0kk= +w w w
2S.D. Hayward, Adaptive beamforming for rapidly moving arrays, in CIE International Conference Proceedings,Oct. 1996, pp. 480--483
DBU Effects
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DBU Effects(Example Simulation)
Conventional ABF
Spatially
MovingJammer
DBU
k= -1
k= 0
k= 1
Inter-CPI Gain
Variation
Spatially
MovingJammer
C t i d DBU
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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
01
H
=
w v
w 0
00
H
=
w 0
w v
=
v 0C
0 v[ ]1 0
T=c
( )1
0 1 1H
=
wR C C R C c
w
0k k= +w w w
2
,1
,
minH
k
H
k k n
k n
=
w v
w x
C t i d DBU R lt
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Constrained DBU Results
Conventional DBU Constrained DBU
k= -1
k= 0
k= 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
Constrained DBU with
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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 oversidelobes
Constrained DBU
k= -1
k= 0
k= 1
Constrained DBU w/ CNL
2
11
1 1cl cl
kK
k kK K
=
R R
=
v 0 u
C 0 v
2
11 0
T
=
c
O tli
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Outline
Colored Noise Loading for Low Sidelobes
Constrained DBU for stable tracking of jammer motion
Data Results
Conclusion
Ft Huachuca GMTI Displays
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Ft. Huachuca GMTI Displays
SAR Image (1m resolution)
Range/Doppler Detection
Range/Doppler Cluster
Range/Angle Localization
GPS Ground Truth
Jammer Angle
07/24/04 CPI# 98045687
GMTI Movie
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GMTI Movie
Range/Doppler Detection
Range/Doppler Cluster
Range/Angle Localization
GPS Ground Truth
Jammer Angle
Desired Beams JammingAngles
07/24/04 CPI# 98045687 98047507
Selected Frames
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Selected Frames
Doppler Aliased
Clutter Filling in
Jammer Null
Close-In
Detection
07/24/04 CPI# 98046337 & 98046437
Tapered Steering Vector (TSV) Comparison
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p g ( ) p30dB Taylor
TSVUndernulled
Jammer false
alarms
New ABF
Standard ABF Comparison
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Standard ABF Comparison
New ABFReg. ABF
Sidelobe
False Alarms
Conclusions
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Conclusions
Propose two ABF modifications
Colored noise loading for low sidelobes with inequalityconstraints to ensure mainbeam interference suppression
Constrained DBU for constant aimpoint gain withnonstationary interference
Both techniques may be utilized together to form a robust
ABF algorithm Demonstrated performance enhancements on data relative to
standard adaptive beamforming techniques
May be applied to multi-channel SAR, GMTI, and SONARdata