lofar rfi mitigation spatial filtering at station level

RFI2010 Workshop, Groningen, Nl, March 29-31, 2010 - 1 -

LOFAR RFI Mitigationspatial filtering at station level

Albert-Jan BoonstraMark Bentum

Mathheijs Eikelboom


Contents

• LOFAR overview• Spectrum environment• RFI mitigation in LOFAR• Data model, spatial filtering algorithm• Spatial filtering in LOFAR, considerations• Spatial filter results• Conclusion


LOFAR signal processing, overview

LOFAR: fsky ~ 30 – 240 MHz

BlueGenecentral

ProcessorCEP

(correlator)

LBA

HBA

RSP

receiver

antennabeam

stationbeams

synth.beams

1 x 32 MHz

High Band Antenna

Low Band Antenna


LBA antenna layout

Operational Oktober 1, 2006 with “final” prototype hardware at Exloo96 dual-dipole LBA antennas distributed over ~500m:• one cluster with 48 dipoles • three clusters of 16 dipoles

Total 24 microstation, 4 dipoles each

Goal: emulate LOFAR with 24 micro-stations at reduced bandwidth or act as a single station at full BW

Exloo

R.Nijboer 2006

LOFAR CS1 configuration 2006-2008 – Exloo

LBA CS10


LOFAR status

Stations 18 core stations + 18 remote stations + 8 int.

Validated: 14 CR, 6 RS

In progress: 6 CS, 1 RS, 3 German, 1 French

Next: 9 RS, 1 UK, 1 Germany, 1 Sweden


LOFAR numbers

Number of sensors on the various fields:Core station fields (18)

96 Low Band Antennas, 2 x 24 High Band Antenna Tiles (HBA field is split)Remote station fields (18)

96 Low Band Antennas, 48 High Band Antenna TilesMicrobaromater (infrasound)

Geo-Remote station fields (10)Geophones & Microbarometers International station fields (8)

96 Low Band Antennas, 96 High Band Antenna Tiles

Numbers for the LOFAR telescope performanceFrequency range: 30- 80 MHz and 120 - 240 MHzPolarisations 2Bandwidth 32 MHz (currently 48 MHz investigated)Stations: 18 core, 18 remote, 8 internationalBaseline length: 100 m to 1500 kmSimult. dig. beams: 8Sample bit depth: 12Spectral resolution: 0.76 kHz


Some LOFAR imaging results

High-resolution LOFAR 3C61.1 imageCredit: Reinout van Weeren (Sterrewacht Leiden) 8 feb 2010

Cas A, Sarod Yatawatta 23 Dec. 2009

LOFAR HBA tile all-sky imageMichiel Brentjens, 22 nov. 2007

20102009

2008

2007

LOFAR all-sky imageStefan Wijnholds19 Nov. 2008

LOFAR LBA alll-sky image Sarod Yatawatta & Jan Noordam 20 April 2007

Deep LOFAR HBA Image Sarod Yatawatta, 21 Feb. 2008

LOFAR all-sky imageStefan WIjnholds25 June 2006

20062007

2008

2004


Spectrum environmentSpectrograms 2009 FM band IIAM

TV band I

Lopik

weathersat.

DABTV#6,7,...

pagerambu-lance,taxi

mariphone

geostat.mil.

satelliteTV band III / DAB (DVB)

FM band II

aviationRAS

RASland

mobileland mobilemobileland

mobile

frequency (MHz)

frequency (MHz)


R

LOFAR overview

spectral estimation:multiply one arm per interferometerwith:

eit

Rclean = R-R

spectral estimation:

derive for AM from R

Spatial filtering:wnew = P w

LOFAR stationon-line correlator

One covariance matrix R per second

512 subbands correlated in ~8.5 min.


The radio spectrum: occurrence of weak RFI

1 minute: ~ 0.02 dB

relatively few weak RFI sources:“horizon effect”

LOFAR High Band Antennavar(R11) = 4 / N

Tsky = 333 KNf = 256Nt = 300 = 60 st = 5 hf = 0.76 kHz


The radio spectrum: effect on data transport rate

Purpose: - increase the number of beams from 8 to 24 (both for 24 MHz bw)- Without increase of station output data rate- Solution: reduce data rate to the LOFAR central processor

from 16 to 4 bits (complex) for each beamLoss when using 4 bit could be solved by spatial filters at stations, but only for fixed transmitters (“fast moving nulls” would hamper calibration)

Experiments in cleanest part of the spectrumindicated that < 10% of the data would be lost(no spatial filtering applied).

e.g. L2007-0189525 HBA bands:In 3 of 23 bands:loss @ 16 bits: 0% @ 4 bits ~ 50%In 20 of 23 bands:no lossAverage loss: 6.5%


Requirement: “smooth” station beamshape changes

Credit: Sarod Yatawatta, ASTRON

LOFAR CS1 calibration/imagingObservation:

• 16 single-dipole stations, 48 h• 20 subbands, each 0.14 MHz

“Calibrated”: removing phase drift (uv)“Residual”: peeling CasA and CygA

LOFAR ITS 2004 observations60 antennas, 26.75 MHz, basel.<200 m• with transmitter (left)• after subtraction filtering (right)• after projection filtering (middle)

LOFAR spatial filtering• At stations, filters for fixed directions (at subband level, ~ 200 kHz)• Post correlation: offline spatial filtering (in ~1 kHz channels)


Data model – signal model

Consider an array of p antennas with baselines :

bij = ri-rj

Array output signals xi(t) and the noise signals ni(t) (from LNAs, spillover etc) are stacked in a vector:

x(t) = [x1(t), … , xp(t)]t, n(t) = [n1(t), … , np(t)]t

Suppose there is one source (astronomical or RFI) with signal s(t) from direction s, and with spatial signature vector a:

The signal vector is defined by:

x(t) = a s(t) + n(t)

t

t


Data model – covariance model

Define the signal covariance sample estimate (observational data):

R = xn xnH with xn = x (nTs)

Given i.i.d. noise vector n(t), E{n(t)n(t)H} = n2 I, and E{s(t)}2 = 2:

R = E{R} = 2 a aH + n2 I

Data model easily exended to multiple sky sources and multiple RFI sources

Complication: low frequency sky contains strong extended structures

Solution: extend model, use baseline restrictions, factor analysis algorithmsHowever: this is not always a problem, e.g. in estimating DOA of strong RFI

n=1

N

^ ^


Data model with sky sources having covariance Rv, and interference with power r2

and signature vector ar: R = Rv + n

2 I + r ar arH

Spatial filtering using projections

Projection matrix: P = I – ar (arHar) -1 ar

H note: Par = 0, Pa ≠ 0

Applying projection: R = P R P

Spatial filtering using subtraction:

R = R – r2 ar ar

H

Note: the subtraction filter can be rewritten as a projection filter by adding a scaling factor , dependent on the noise and on the RFI power:

P = I – ar (arHar)-1 ar

H

cf. A. Leshem, A.J. van der Veen, and A.J. Boonstra. Multichannel interference mitigation techniques in radio astronomy. The Astrophysical Journal Supplement Series, 131(1):355–373, November 2000.

Spatial filtering after correlation

~ ^

~ ^


Beamforming and spatial filtering

narrow band beamforming

Source power: IB = Ryy = E{yyH} = wHE{xxH}w = wHRw=> station sky map Ib(s)

Recall data model: R = s2 aaH + n

2 I

Maximum if w = a: wHRw = s2 wHa aHw + n

2 wHw

beamfomer output y=wHxto central processor (BlueGene)

local processing: station correlator, one second integrated R every 512 seconds- used for station calibration and RFI mitigation

Spatial filtering (beamformer impl.), with Pa spatial filter: w’ = P wAnd: IB = wH PRP w

… y = wHx

x1

x2

xp

w1

w2

wp


How to find DOA and time-occupancy

DOA:• From a subspace analysis, R =U U:

• Finding maxima in sky maps• Transmitter locations may be known• Using factor analysis, efficient rank-one methods

ITS data

Credit: M.Tanigawa& M.Moren

How to assess the time-occupancy oftransmitters: sorting eigenvalue spectra and make daily percentile plots of number of eigenvalues above threshold


Spatial filtering, LBA station at 50.2 MHz

One-hour LOFAR LBA spectr (left) and one-day duration Frobenius norm spectrogram (right).

Data: 1 second integrated LOFAR station subbands, every subband is updated once per 512 seconds


Spatial filtering, LBA station at 50.2 MHz

LBA station eignevalues @ 50.2 MHz (upper)

LBA station spectrogram (upper left) and same data after spatially filtering, based on first time slot at 50.2 MHz (lower left)

After one hour a second transmitter at a different direction emerges


Integration time = 60 s

Filter: one dimension is projected out

Spatial filter suppession: 20 dB (right figure)

2nd obs: second 60 sample @ filter of previous time slot, result: no suppression

Note: HBA station data is correlated by CEP, forming ~1kHz channels

Spatial filtering, HBA station at 143.75 MHz



Fixed spatial projection filter estimated from and applied to first 60 s integration time (right)• 16 dB suppression, one subspace dimension removed• 38 dB suppression after two dim. Removed (not shown)• 4 dB supp using spatial filet of first time 60 s slot (not shown)Air traffic band: moving transmitter or strong changes in propagation



Fixed spatial projection filter estimated from and applied to first 60 s integration time (right)• 10 dB suppression, one subspace dimension removed• 6 dB supp using spatial filter of first time 60 s slot (not shown)• 1 dB supp using spatial filter after one hour (not shown)Somewhat erratic suppression numbers over time


fixed spatial projection filter (one dim) estimated from and applied to first 60 s integration time (upper right), and applied after 8 hours (right)



Channel without RFI(phase fluctuations partly due tot sky) Channel with RFI



LOFAR Core Station, LBA antennas

subspace analysis:eigenvalues

Frobenius norm spectrogram

October 2009


Core station: off-line spatial filtering

spectra before filtering spectra after filtering



before filtering after filtering, filter update every snapshot



after filtering, only one filter settingafter filtering, filter update every snapshot


Conclusions and next steps

• No accumulation observed of weak RFI @ -240 dBWm-2Hz-1 levels (horizon effect)

• Experiments indicated that station output signal data rate reduction (16 to 4 bit) would lead to < 10% data loss in cleanest parts of the spectrum

• Fixed spatial filters can be applied at station level to suppress fixed transmitters– LOFAR systems are stable enough, performance will be

improved by applying station calibration

• Coming year: RFI direction inventory

• At some later stage: reconsider station spatial filter updates every second using filters with constraints at the direction of the strongest “peeling sources”

lofar rfi mitigation spatial filtering at station level

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