some signal processing perspectives for calibrating phased ... · some signal processing...
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Some Signal Processing Perspectives for Calibrating Phased Arrays
Prof Douglas Gray
Director UoA Radar Research Centre
Phased arrays examples
Why calibrate ?
Internal calibration
Self calibration
Calibration using sources of opportunity
Calibration using other sensors
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Some phased arrays
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Array Errors
transducerReceiver
unit
transducerReceiver
unit
transducerReceiver
unit
Calibration - anechoic chambers not always feasible
Gain and phase mismatch between receivers
constant across the receiver bandwidth
varying across the receiver bandwidth
Positional errors
Unknown mutual coupling
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Why Calibrate ?
Key issue for
Maintaining beam pattern and hence SNR
Low sidelobe phased arrays
Advanced beamforming algorithms
DoA estimation
Maintaining dynamic range
Off-axis polarimetric biases
Not always necessary
Some advanced interference rejection techniques don’t require it !!
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Sidelobe sensitivity
Increase in SLL due to phase errors
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Sensitivity of MUSIC to Modelling Errors
The MUSIC ―spectrum‖ in the presence of phase errors, 0.005<β<0.05, the two sources are at directions
γ=13.5 and 16.5 degrees (4 uniformly spaced circular sensors, half a wavelength apart)
Performance of MUSIC degrades when the array manifold is not accurate
=> Calibration will improve sensitivity
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Application Areas
Radar : gain and phase errors, mutual coupling
GPS : gain and phase errors, mutual coupling
Sonar : gain and phase errors across a range of frequencies
positional errors for towed arrays
Radio Astronomy : : gain and phase errors VLBI arrays
Communications : gain and phase errors
Synthetic aperture radar, ISAR : motion compensation, autofocus
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Some Key Issues
Parameters to calibrated
Amplitude and phase errors
Mutual coupling
Receiver positioning errors
Cross polarimetric terms
Quality of calibration
Dependent on type of processing
eg phase errors typically to within
10—30 degrees for conventional beamformers
5-10 degrees for optimum beamformers
<5 degrees for high accuracy DoA estimators
eg MUSIC
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Calibration Techniques
Various signal processing techniques for calibration
Using internal calibration signals
Self-calibration – known as equalisation
Using sources of opportunity
Single sources
Multiple sources
Using other sensor information
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OTHR – “Internal” calibration
transducerReceiver
unit
(b) calibrate by injecting signals at receiver
Array ~2.78 Kms in length
(a) calibrate using a nearfieldsource
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Some Necessary Theory
Signal Processing Techniques for Calibration
but first
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Vary the steering direction
)()(1
.exp)(1),(1
fXkvK
ukjfXK
fkY
H
K
k
kk
Kukj
ukj
ukj
e
e
e
kv
.
.
.
2
1
)(
where
k
Ku
2u1u
Phase Shift Beamforming
Frequency domain
General Array
Geometry
Steering vector
Set of all steering vectors forms the
“array manifold”
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Block Diagram
)(1
fX )(2
fX )(3
fX )( fXK
),( fkY
Kv*
1
Kv*
2 Kv*
3 KvK
*
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Mean Output Beam Power
Cross-spectral Matrix
),,()(),,(1
),,(,,2
2fvfRfv
KfYEfP x
H
cbf
)()()( fXfXEfRH
x
Output Beam Power
2
2 ),,()()(),,(),,(
KfvfXfXfv
fYHH
Squaring
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Cross-spectral Matrix
)()()()()()(
)()()()(
)()()()()()(
)()()(
)(
)(
)(
)()()(
21
2212
12111
21
2
1
fXfXEfXfXEfXfXE
fXfXEfXfXE
fXfXEfXfXEfXfXE
fXfXfX
fX
fX
fX
EfXfXEfR
KKKK
K
K
K
H
x
Elements are the spatial covariances
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CSM Examples I
Uncorrelated receiver noise
IffR
ffNfNE
nx
ijnji
)()(
)()(*)(
2
2
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CSM Examples II
Plane wave signal
)()()(
)()()(*)(
)()()(
2
s
H
ss
s
H
s
H
x
kvkvf
kvkvfSfSE
fXfXEfR
)()()(
)()()(
s
s
kvfSfX
kvtstx
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CSM Examples III
)()()(
)()()(
fRfRfR
fnfsfx
nsx
Plane wave signal in white noise
)()()()()( 22
s
H
sinxkvkvfIffR
Example of an important principle
For uncorrelated processes
Defines signal subspace Defines noise subspace
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Finally
The CSM plays a pivotal role in
(a) Optimum beamforming
(b) High resolution DOA algorithms
(c) Calibration techniques
(d) Defines signal and noise subspaces
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Self-referencing
Adaptive equalisation or spatial prediction
(M Trinkle : GPS, G Frazer OTHR, J Horridge Acoustic Radar)
Choose weights to
minimise output
backward/forward
prediction error
power for each
array channel+
1z
1z
1z
][kxref
x
x
x
x
x
][kebf
]2/[ Nkx j
1w
2w
3w
1w1z
x
2w
3w
+ ][kx j
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GPS – adaptive digital equalisers
Channel Mismatch < 0.05 dB Difficult to achieve good analogue mismatch with 50 dB out of band rejection
Measured Interference cancellation ratios
2 4 6 8 10 12 14 16 18 20 22 24-0.5
-0.2-0.1
00.10.2
0.5
1
Frequency (MHz)
Mis
matc
h (
dB
)
Channel Mismatch Over Signal Bandwidth
Before EqualisationAfter Equalisation (7 taps)
Mismatch < 0.05 dB
0 5 10 15 20 25-10
0
10
20
30
40
50
60
70
80
Frequency (MHz)
Pow
er
(dB
)
Input SignalCancellation After EqualisationNon-Interference Signal
Noise floor
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Self-referencing ; Radio Astronomy
Phase errors only for illustration (easily extended to include amplitude)
Assume a initial model for the exact covariance matrix,
e.g., )()( H
x vvR
KKKK jj
jj
jj
j
j
j
j
j
j
ee
ee
ee
e
e
e
v
e
e
e
v
22
11
2
1
2
1
~
~
~
~
Compute the estimated covariance matrix
Phase errors to be
estimated
Refine the model and iterate
for a coherent point source
xR
2
,,,
ˆ
lk
lkx
j
lkx ReR lk
Estimate the phase errors, as those that minimise k
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VLBI Example
P. J. Napier, R. T. Thompson, R. D. Ekers,
Proc. IEEE 71, 1295 (1983).
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Originally formulated for optical astronomy (Muller and Buffington 1974)
Applied to sonar (Bucker 1978, Ferguson 1989)
One Definition (several variants)
Sharpness
)sin,,max(argˆ0
2
dxfPSx
set of unknown parameters, eg receiver positions, phasesx
xfP ,, Output of conventional or optimum beamformer
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Sources of Opportunity
Signal Subspace Methods
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A simple eigen-based phase calibration technique
Key Idea
A single strong far field source of opportunity
Compare the measured phases with those predicted
Estimating the phases
Estimate the CSM
Use of eigenvector corresponding to largest eigenvalue CSM
Some maths
H
s
H
sinx EkvkvEfIffR )()()()()( 22
ekVkvEu ss )()()( max
[D. A. Gray, W. O. Wolfe and J. L. Riley, “An eigenvector method for estimating the positions of the elements of an array of receivers”, Australian Symposium on Signal Processing Applications, 1989, Gold Coast, Australia, Vol. 2, pp 391-393.
If the noise is directional then need to prewhiten
)()( ss kvEkv E is diagonal matrix of amplitude
and phase errors
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Maintaining SNR
Sonar arrays depart significantly from linearity during manoeuvres
Use of targets of opportunity to measure and correct for positional
errors
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Sources of Opportunity
Noise Subspace Methods
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Friedlander’s approach I
Extension
Multiple signals of opportunity
Unkown DOAs
Incorporates mutual coupling
)()()()()( faCEVfXkvfafX
)(:)(:)( 21 NkvkvkvV Matrix of steering vectors
E is diagonal matrix of amplitude and phase errors
C is matrix of mutual coupling coefficients
B. Friedlander and A. J. Weiss, “Direction finding in the presence of mutual coupling”, IEEE Transactions on Antennas and Propagation, March
1991, Vol. AP-39, pp. 273-284.
B. Friedlander and A. J. Weiss, “Eigenstructure methods for direction finding with sensor gain and phase uncertainties”, ICASSP, 1998, New
York,U.S.A., pp 2681-2684.
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Friedlander’s approach II
Key Observation
MUSIC DOA estimation algorithms extremely sensitive to manifold errors
Perturbed signal subspace orthogonal to noise subspace
Thus columns of the matrix CEV are orthogonal to the noise subspace
Freidlander’s metric2
1,,,,
)(ˆmin21
L
n
n
H
ECwrt vCEUL
U
Neat three stage iterative algorithm
Matrix of estimated noise subspace eigen-vectors
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Disjoint sources
Daniel Solomon’s PhD thesis
Use back scattered returns from ionised trails of meteors as they burn up on entering the atmosphere
Friedlander’s approach of using subspace methods to estimate DOAs, array positional errors and mutual coupling coefficients
Range and time gating used to separate sources (disjoint)
Cost function modified to 2
1,,,,
)()(ˆmin21
L
n
n
H
ECwrt vCEnUL
Matrix of estimated noise subspace eigen-vectors for each source)(ˆ nU
I. S. D. Solomon, D. A. Gray, Y. I. Abramovich, and S. J. Anderson, Overthe- horizon radar array calibration using echos from ionized me- teor trails, Proc. Inst. Elect. Eng. Radar, Sonar, Navigation, vol. 145, pp. 173180, June 1998.
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Calibration – HF array
Sidelobe
comparison
Variant developed by Zili Xu for calibration of GPS antenna using GPS satellites as sources of opportunity
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Frequency dependent calibration
Gain and phase responses vary across
operating bandwidth
Limits dynamic range in presence
of strong interferences
Increases dimension of signal subspace
At each range bin find a matrix
that minimises a time averaged error
between the range bin in question
and a reference range bin.
Uses strong AM interferences
G A Fabrizio, D A Gray and M D Turley "Using Sources of Opportunity to Estimate Digital
Compensation for Receiver Mismatch in HF Arrays" IEEE Trans on Aerospace and
Electronic Systems. (AES), Vol. 37, No. 1, Jan 2001, pp310-316.
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Oracular S. McMillan and Y Abramovich
Calibration of amplitude and phase for an OTH HF radar
Uses LOS active returns from ISS ―Zarya‖ space station.
Key point : Moving target of opportunity over a CIT
Technique
Estimate signal and noise CSM
Prewhiten using estimated noise CSM
Decompose signal CSM into the ―signal‖ and ―noise‖ eigen-spaces
(Use of Slepian kernel to determine dimension of signal subspace)
(Signal subspace is the space spanned by the steering vectors of
the moving target over the CIT)
Form an ―integrated projection operator‖ onto noise subspace
Amplitude and phase errors obtained from the eigenvector
corresponding to the minumum eigenvalue of this operator
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ISAR Calibration
Coarse translational motion
compensation of target/range
realignment
Autofocus – fine motion
compensation/phase realignment
Four techniques for estimating
the phase correction across PRIs
Max CBF, Max MVDR,
Max projection onto signal subspace,
Min projection onto noise subspace
All subject to a unity norm constraint
Z. She, D A Gray and R E Bogner. "Autofocus for Inverse Synthetic Aperture
Radar (ISAR) Imaging" Signal Processing, Vol 81, 2001, pp275=291
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Using instrumentation
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Calibration – towed sonar array
Sonar arrays depart significantly from linearity during manoeuvres
Use of compass data and model of array dynamics
Design Kalman filter to estimate the time varying array shape
Incorporate unknown biases in compass outputs into the KF estimates
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Summary
Novel solutions, based on signal processing, for calibrating large
arrays of receivers
Wide variety of techniques across a number of different
application areas
Self-referencing or model based approaches
Sources of opportunity
Instrumented arrays
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Closure Phase
Key Idea
Sum of phases around a closed loop of 3 receivers is independent of
the phase errors.
Provides info regarding source
eg if the sum is zero then the source is highly coherent.
Also for highly redundant arrays in homogeneous noise fields can be
used to constrain individual phases to lower dimensional subspaces.
Amplitude closure as well.