Array Processing

SPATIAL ARRAY PROCESSING METHODS FOR RADIO ASTRONOMICAL RFI MITIGATION

Timely Technical Tutorials, URSI Boulder 2013, J2

Brian D. Jeffs and Karl F. Warnick Brigham Young University

Many array-based instruments suitable for spatial filtering are now in use, and new classes and forms are on the way

Applications in Radio Astronomy

Synthesis Imaging Array Spatial Filtering

Image credit: Dave Finely, National Radio Astronomy Observatory / Associated Universities, Inc. / National Science Foundation.

¨  Arrays such as the EVLA, WSRT, GMRT, etc. are well suited for adaptive array processing.

¨  The full array can be configured as a nulling beamformer. ¨  Array elements are the single dishes, or a station beam.

Synthesis Array Spatial Filtering

High gain main antenna arrayLow gain auxiliaryantennas

q

pI(p,q)

s(i)

A(p,q)

uwv

r1r2 rM

Interferingsatellite

Deep-spaceobject

y (i)m

y (i)a

Ground-basedtransmitter

z (i)1

z (i)2

m

¨  Post-correlation processing removes interference from visibilities.

¨  Large aperture means tracking speed is an issue. Need short integration dump times.

¨  Dish directivity helps and hurts: partially rejects RFI, but reduces INR needed to estimate interference subspace.

¨  Auxiliary antennas help.

Phased Array Feed Spatial Filtering

y(i)

s(i)z(i)

Radio telescope dish with a phased array feed

¨  Just now being deployed.

¨  Small array aperture helps with moving interferer tracking.

¨  Beamshape distortions may be a critical problem.

¨  Dish directivity helps and hurts cancelation.

¨  Must cancel sources outside the FOV in dish sidelobes.

Aperture Arrays

e.g. a LOFAR station aperture array with 96 antennas Images copyright ASTRON

Interior view showing four dual pol broadband dipole elements

Aperture Arrays

¨  Low frequency: LOFAR, LWA, MWA, PAPER, etc. ¨  Parabolic dish collecting area is replaced with a

“station” of closely packed simple antennas ¨  Wide fields of view for station elements make them

highly susceptible to RFI from horizon to horizon. ¨  Non SOI deep space objects must be treated as RFI

and cancelled (or peeled). ¨  Tracking is slow for station beams, fast for full

array.

These have always been promising, but have issues in the RA application

Basic Processing Structures

¨  Signal Model:

The Narrowband Beamformer

+

w*1

w*3

w*M

b(i) = w y(i) H

!

Space signalof interest

Noise :

Interference

y (i)1

y (i)3

y (i)M

n (i)1

n (i)M

s(i)

z (i)1

¨  Repeat for each frequency channel.

¨  w is (weakly) frequency dependent. y(i) = a s(i)+ vdd=1

D

∑ (i)zd (i)+n(i)

Time-dependent Covariance Estimation

¨  Calculating w for spatial nulling relies critically on array covariance estimation. ¤ Definitions:

¤ Must identify the interferer vector subspace portion Rz

¨  Must update frequently to track interferer motion ¤ Compute for all short term integrations (STI), k. ¤ STI windows are Nsti samples long, which depends on

motion rate and aperture size.

R = E{y(i)yH (i)} =Rs +Rn +Rz

Rk =1N

y(i)yH (i)i=kN

(k+1)N−1

∑

Rk

Classical Adaptive Canceling Beamformers

¨  Maximum SNR beamformer ¤ Maximize signal to noise plus interference power ratio:

¤  Point source case yields the MVDR solution:

¨  LCMV beamformer ¤ Minimize total variance subject to linear constraints:

¤ Direct control of response pattern at points specified by C. ¤ Can also constrain derivatives (slope) or eigen vectors.

wsnr = argmaxwwHRsw

wH (Rz +Rn )w→ Rswsnr = λmax (Rz + Rn )wsnr

wmvdr = (Rz + Rn )−1a

w lcmv = argminwwHRw s.t. CHw = f→ w lcmv = R

−1C[CHRC]−1f

−100 −80 −60 −40 −20 0 20 40 60 80 100−80

−70

−60

−50

−40

−30

−20

−10

0

10

Bearing in degrees

Res

pons

e in

dB

rela

tive

to p

eak

Conventional, Kaiser windowedMax SNR, no interferer in Rn model

Max SNR, interferer at −10 deg

An Example of MaxSNR canceling

¨  Very high INR case, +70 dB. ¨  SNR = +40 dB

¨  Max SNR output SINR = 50 dB

¨  10 element ULA

¨  Exact covariances

¨  Output SIR is 139 dB!

Problems with the RA Signal Scenario

¨  SOI and interferer are well bellow the noise floor. ¤ SNR of -30 dB or worse is common. ¤  INR <0 dB can still severely corrupt SOI. ¤ Extremely hard to estimate Rz from R.

¨  Motion limits integration time, increases sample estimation error in Rz.

¨  Weak but troublesome interferers yield shallow nulls. ¨  Canceling distorts beam beampatterns.

¤ Raises confusion limit in sidelobes. ¤ Main beam may not have known shape.

Realistic Example of MaxSNR Canceling

¨  Low INR case, -10 dB. ¨  SNR = -40 dB

¨  Input SINR = -40 dB ¨  Max SNR output SINR = -30 dB

¨  10 element ULA

¨  Exact covariances

¨  Output SIR = -5 dB

−100 −80 −60 −40 −20 0 20 40 60 80 100−80

−70

−60

−50

−40

−30

−20

−10

0

10

Bearing in degrees

Resp

onse

in d

B re

lativ

e to

pea

k

Conventional, Kaiser windowedMax SNR, no interferer in Rn model

Max SNR, interferer at −20 deg

Pattern Distortion with Moving RFI ¨  While adapting to null a moving interferer, sidelobe structure is

unpredictable. ¨  Becomes severe as null approaches the main lobe. ¨  Even sidelobe “rumble” increases confusion noise, hampers on – off

subtraction.

¨  Uniform line array.

¨  2 moving interferers, starting at +33 and -35 degrees, then moving to the right.

¨  0 dB INR

Achieving deeper cancelation nulls

Improved Methods for RA

The Good News

¨  Real-time correlators are already needed for all array types (imaging, aperture, PAF) ¤ Used to calculate visibilities or calibrate beams. ¤ Just need rapid dump times to handle motion ¤ Additional computational for spatial filtering is small

¨  Much progress has been made to address null depth limitations in the RA scenario.

Subspace Projection

¨  Well suited for synthesis arrays, aperture arrays, PAFs, and post correlation processing.

¨  Zero forcing, deeper nulls than with total variance minimization. ¨  Must assume interference is the dominant source. ¨  Use eigenvector decomposition to identify the interference subspace. ¨  Partition eigenspace. Largest eigenvalues(s) correspond to

interference.

¨  Form perpendicular subspace projection matrix:

¨  Compute weights and beamform:

€

ˆ R k[Uint | Usig+noise] = [Uint | Usig+noise]Λ

€

Pk = I−UintUintH

wSSP,k = Pkwnominal, b(i) =wSSP,kH y(i), k = i

N!

"!#

$#

Real-World PAF Subspace Projection

¨  19 element L band PAF on Green Bank 20 Meter Telescope. ¨  Snapshot radio camera image, 21 by 21, 441 simultaneous beams. ¨  CW interference, hand held antenna and signal generator walking in

front of Jansky Lab.

Cross Elevation (Degrees)

Elev

atio

n (D

egre

es)

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

-10

0

10

20

30


Elev

atio

n (D

egre

es)

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

0

20

40

60

80

100


Elev

atio

n (D

egre

es)

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

-10

0

10

20

30

W3OH, no RFI RFI corrupted image (moving function generator and antenna on the ground)

Adaptive spatial filtering Subspace projection algorithm

Subspace Bias Correction

¨  Projecting out interference subspace distorts the beampattern. ¨  Leshem and van der Veen proposed a correction for moving

interference over the N sample long term integration (LTI).

¨  Well suited for satellite RFI at large imaging arrays. ¨  Use directly as an imaging visibility matrix.

¨  RFI is removed from each STI, but in no subspace is missing.

Rk = PkRkPk

H

R = unvec B−1 vecRk

k=0

J−1

∑#

$%

&

'(

)*+

,+

-.+

/+

B = P k*⊗

k=0

J−1

∑ Pk, J = N / Nsti12 34

R j

R j

Auxiliary Antenna Methods

¨  Improve interference subspace estimate and increase null depth.

¨  Aux antennas return higher INR signal.

¨  Must track moving interferers.

¨  One antenna per interferer.

Rz

High gain main antenna arrayLow gain auxiliaryantennas

q

pI(p,q)

s(i)

A(p,q)

uwv

r1r2 rM

Interferingsatellite

Deep-spaceobject

y (i)m

y (i)a

Ground-basedtransmitter

z (i)1

z (i)2

m

Aux Antenna Performance Analysis

ï220 ï200 ï180 ï160 ï140 ï120 ï100 ï80 ï60ï60

ï40

ï20

0

20

40

60

Total Interference power in dBm at primary feed

Out

put S

igna

l to

Inte

rfere

nce

Ratio

(SIR

), dB

SPSPACSPMSCNo Processing

¨  Extend array vector to include auxiliaries.

¨  Compute “projection” matrix with SVD on

VLA simulation for two stationary interferers and two small auxiliary dish antennas. Source is 1 Jy OH emission. INR at primary feeds is 146 dB above the plotted dBm interferer power level.

Cross Subspace Projection

y(i) =ym(i)ya (i)

!

"##

$

%&&

R =Rmm Rma

Ram Raa

!

"##

$

%&&

Rma

Rma =UΣVH

Us = [uD+1,,uMm]

PCSP = UsUsH , 0Mm

"# $%

RCSP = PCSP RPCSPH

Bias Corrected Array PSD Estimation

¨  When averaging is performed for a beamformed PSD estimator, or total power observation, beampattern distortion can be completely corrected.

¨  Based on subspace projection beamforming.

Sy,c =αJ(wH ⊗wT )B−1 (Pk ⊗

k=0

J−1

∑ Pk*) (YkGF) (YkGF)

*( )

B = 1J

P k*⊗

k=0

J−1

∑ Pk, J = N / Nsti$% &'

Pk = I−UintUintH , Rk[Uint |Usig+noise ]= [Uint |Usig+noise ]Λ

Where F is the Fourier transform matrix, G is a diagonal window weighting matrix (e.g. Hamming window), α= 1/(diag{G}T diag{G}), and

Yk = y(k Nsti ), y(k Nsti +1),, y([k +1]Nsti −1)[ ]

Bias Corrected Array PSD Results

ï4 ï3 ï2 ï1 0 1 2 3 4ï20

ï15

ï10

ï5

0

5

10

15

Frequency in rad/sample

Pow

er/fr

eque

ncy

(dB

/rad/

sam

ple)

Power Spectral Density using all data via Welch’s method

Subspace ProjConventionalBias corrected subspace

2 interferers, weak SOI, J = 900, 512-point STIs

Bias Corrected Array PSD Results (cont.)

ï100 ï80 ï60 ï40 ï20 0 20 40 60 80 100ï80

ï70

ï60

ï50

ï40

ï30

ï20

ï10

0

10Effective beam response in PSD estimate

Bearing in degrees

Bea

m re

spon

se in

dB

Bias corrected proj.Subspace projectionConventional

¨  Effective beampatterns as seen by the PSD estimators (i.e. these are averaged over all STIs due to the PSD calculation.)

¨  Bias corrected PSD matches the conventional fixed beam response, even while canceling interference.

¨  Time varying adaptive beampattern.

Conventional SSP Limitations

101 102 103 1040

20

40

60

80

IRR

= P

int,i

n / P in

t,BF d

B (s

olid

line

s)

IRR & Residual Interference Power, motion = 2 °/sec

STI length (samples)

101 102 103 104 −160

−130

−100

−70

P int,B

F dBm

(dah

sed

lines

)

INR = 60dBINR = 40dBINR = 20dBINR = 0dBINR = −20dB

¨  Detailed simulation ¨  19-element PAF on

20m reflector, 0.43f/D ¨  Correlated spillover

noise, mutual coupling, modeled 33K Ciao Wireless LNAs.

¨  Measured array element radiation patterns.

¨  Physical Optics, full 2D integration over reflector.

Subspace estimation error due to sample noise from short STI

Subspace smearing error due to motion, with no sample estimation error.

¨  Fit a series of STI covariances to a polynomial that can be evaluated at arbitrary timescale. ¤  Beamformer weights can be updated every time sample. ¤  Use entire data window to fit polynomial for less sample estimation error.

¨  Minimize the squared error

between STI sample covariances and the polynomial model CLS:

Low Order Parametric Models for SSP

CLS = argminC

Rk − Rint (tk,C)k=1

K

∑F

2

,

where tk=kNstiTs

105−180

−170

−160

−150

−140

−130


Res

idua

l Int

erfe

rer P

ower

(dB

m)

Residual Interferer Power, motion = 3°/sec

Conv−SPEIG−7−SPOPT−7−SP

Rk

Rint (t,C) = apoly (t,C)apolyH (t,C)

t=nTs

where C = [c0cr ]apoly (t,C) = c0 + c1t ++ crt

r

Real Data Cancelation Results

102 103 104 105

−160−155−150−145−140−135−130−125−120−115


Res

idua

l Int

erfe

rer U

pper

Lim

it

Tx near boresight, Slow MotionINR −12dB, Poly = 8, with Pre−Whitening

MaxSens on−offConv−SP on−offEIG−8−SP on−off

102 103 104 105

−160

−150

−140

−130

−120


Res

idua

l Int

erfe

rer U

pper

Lim

it

Tx in sidelobes, Slow MotionINR −12dB, Poly = 8, with Pre−Whitening

MaxSens on−offConv−SP on−offEIG−8−SP on−off

50 100 150 200

−160

−150

−140

−130

−120

−110

PSD bin

PS

D

Tx in sidelobes, Slow MotionINR −12dB, Poly = 8, with Pre−Whitening

MaxSens on−offNoise onlyConv−SP on−offEIG−8−SP on−off

50 100 150 200

−160

−155

−150

−145

−140

−135

−130

−125

−120

−115

PSD bin

PS

D

Tx near boresight, Slow MotionINR −12dB, Poly = 8, with Pre−Whitening

MaxSens on−offNoise onlyConv−SP on−offEIG−8−SP on−off

Final Observations

Additional Methods

¨  Robust beamforming ¤ Helpful if array calibration has errors. Keeps SOI from

being canceled. ¨  Spectral scooping correction

¤ Block mode covariance matrix processing for beamformer weight updates.

¤ Narrowband RFI. ¤ Surprising behavior: the time-varying spatial filter

places a spectral null in PSD estimates. ¤ The cause of this bias has been described analytically. ¤ A straightforward correction has been proposed.

Progress Towards Adoption

¨  Parks Telescope adaptive filter for digital TV may be the only current production system.

¨  Despite significant promise and potential, adoption has been slow.

¨  Perhaps astronomers are reluctant to move from the tried and true.

¨  We have not yet identified the critical science case. ¨  Computational and infrastructure costs are

incremental, but non-trivial. ¨  I hope presented progress in overcoming spatial

filtering limitations will spur adoption.

Conclusions

¨  RFI spatial Filtering for radio astronomy offers a new mode of mitigation beyond time blanking, frequency excision, and avoidance.

¨  It is challenging due to low INR and SNR. ¨  Algorithms common to wireless comm., radar, and sonar

do not drive deep enough nulls, and distort beampatterns.

¨  Several algorithm enhancements have been proposed which may solve these limitations.

¨  Widespread adoption of these promising methods may take some time for confidence to build.

¨  The “critical science case” which cannot be pursued without spatial filtering would spur deployment.

Bibliography

¨  A. Leshem, A.-J. van der Veen and A.-J. Boonstra, “Multichannel interference mitigation techniques in radio astronomy,” Astrophysical Journal Supplement, vol. 131, no. 1, 2000.

¨  A. Leshem and A.-J. van der Veen, “Radio-Astronomical Imaging in the Presence of Strong Radio Interference,” IEEE Jour. on Information Theory, vol. 46, no. 5, Aug. 2000.

¨  B.D. Jeffs, L. Li and K.F. Warnick, “Auxiliary antenna assisted interference mitigation for radio astronomy arrays,” IEEE Trans. on SP, vol. 53, No. 2, February, 2005.

¨  B.D. Jeffs and K.F. Warnick, “Bias corrected PSD estimation for an adaptive array with moving interference,” IEEE Trans. on SP, vol. 56, no. 7, July, 2008.

¨  J. Landon, B.D. Jeffs, and K.F. Warnick, “Model-Based Subspace Projection Beamforming for Deep Interference Nulling,” IEEE Trans. on SP, vol. 60, no. 3, March 2012.

¨  B.D. Jeffs and K.F. Warnick, “Spectral bias in adaptive beamforming with narrowband interference,” IEEE Trans. on SP, vol. 57, no. 4, Apr., 2009.

¨  J. R. Nagel, K. F. Warnick, B. D. Jeffs, J. R. Fisher, and R. Bradley, “Experimental verification of radio frequency interference mitigation with a focal plane array feed,” Radio Science, vol. 42, RS6013, doi 10.1029/2007RS003630, 2007.

¨  S. van der Tol and A.-J. van der Veen, “Application of robust Capon beamforming to radio astronomical Imaging,” Proceedings of ICASSP 2005, vol. iv, pp. 1089-1092, March 2005.

¨  S. van der Tol and A.-J. van der Veen, “Performance analysis of spatial filtering of rf interference in radio astronomy,” IEEE Trans. on SP, vol. 53, no. 3, Mar. 2005.

Array Processing

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