2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 1
Outline
- progress in RFI mitigation (methods inventory)
- system design & RFI mitigation: what and where
System design & RFI mitigationDS4T3 (OPAR, ASTRON, INAF-IRA, UORL, CSIRO)
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 2
Deliverable Achieved1 RFI mitigation methods inventory fall 20072 Influence on data quality Y3Q43 Impact of moving interference sources fall 20074 Cost effect. and tech. Requirements SKA site Y3Q45 Demonstrations with EMBRACE, BEST … Y4Q46 RFI mitigation strategies for the SKA Y4Q4
phased-arrays
System design & RFI mitigationDS4T3 (OPAR, ASTRON, INAF-IRA, UORL, CSIRO)
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 3
RFI Mitigation Methods InventoryReport, June 2007
• Introduction• Spectral selectivity• Temporal selectivity• Spatial Selectivity• Multi-dimensional techniques• Implications for SKA and conclusions
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 4
Figure 1: (a) A classical filter bank implementation. (b) polyphase implementation of any filter. (c)polyphase implementation of a filter bank. (d) Example of filter banks (M=64). The input signal was the sumof an impulse, a sweeping sine wave and 2 switching sine waves: (d.1) h(k) is a 64 long rectangular impulseresponse. The filter bank is just a FFT. (d.2) h(k) is a 64 long Blackmann-Harris window. (d.3) h(k) is 640long low pass filter.
(a) (b)
(c)
(d.1) (d.2) (d.3)
Spectral selectivitypolyphase filterbank
ALMA memo 447 (J. Bunton) for cascaded PFB
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 5
Spectral selectivitynarrow band RFI elimination
Figure 4 : Analysis/synthesis process with quasi perfect reconstruction filter bank. Discrete Cosinustransforms have been implemented. Narrow band interferences are visible on the time-frequencyrepresentation (left). After analysis, polluted channels have been removed and the signal has beenreconstructed through a polyphase synthesis filter bank. The resulting time-frequency plane is given on theright
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 6
Temporal selectivityBlanking
• Detection theory based on hypothesis testing
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 7
Temporal selectivityBlanking
• Single-antenna detection: Pfa and PD are knownPfa = Q(2PD = Q(2/ (1+INR))
• Multiple-antenna (p) detection (spatial-temporal) matched spatial detector: compare the received energy from the
interferer to the noise
test : data covariance matrix, combined with known interferer direction
Pfa : same PD = Q(2/ (1+p.INR))
residual after blanking : INRres 1 / p.N1/2
N: number of samples
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 8
Spatial selectivityFiltering
• Algorithms are based on modifications of data covariance matrix by a spatial filter, such that:
Pkak = 0 (ak direction of interferer)Pk applied to covariance matrix: interferer energy nulled• when ak is unknown ?
=> find eigenvalues and eigenvectors• a correction (matrix) has to be applied to the filtered
covariance matrix• Constraint: astronomical signal power << interferer power• residual after blanking :
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 9
Multi-dimensional techniquesCyclostationarity
cyclostationary process : statistics are periodic with time
Random binary signal: temporal view
covariance : time origin as random
covariance : time origin as constant
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 10
system design & RFI mitigation ASTRON/ISPO SSSM SKA monitoring results 2005/2006
virtual site, i.e. median of maxima of curves from the four sites visited : South Africa, China, Australia, Argentina
Cf. SKA monitoring protocol 2003 S.Ellingson et al (SKA memo)
Number of ADC bits:- 3 to 7 effective bits - depending on f, BW, site- if nonlinearities for short timescales are allowed: only 3 to 5 bits are needed
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 11
system design & RFI mitigationdata transport bottleneck
From RFI & data coding perspective: • use large subband bandwidth from stations to central site• break bands into more subbands / isolate bands with strong RFI• apply (fixed) spatial nulls at station level (“cheap”)• apply parametric techniques (more expensive; specific to coding scheme)
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 12
system design & RFI mitigation effectiveness
Bottom line: one can mitigate RFI down to the level that it can be detected
So: delay RFI mitigation to the last stages in the datastream where data compression reduces the RFI mitigation SP load (beamforming, post correlation integration), unless…
• for dynamic range reasons- linearity requirements of LNAs after BF (PAF)- reduce number of bits (data transport reduction / digital stages PAF/AA)
• RFI is strongly spatially distributed- then local spatial filter makes more sense, at stations or between several stations
• RFI spectral bandwith does not match channel bandwidth- all methods
• RFI temporal characteristics - excision of s bursts close to antenna; drawback: loss of gain information
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 13
system design & RFI mitigation effectiveness
Stacking of methods is not usefull unless …...different domains are combined, e.g.
• RFI source subtraction, sidelobe cancelling and spatial filtering in
arrays are all spatial methods – in general not much use combining them
• parametric methods (Glonass/DVB suppr., Ellingson et al) and spatial filtering
2nd SKADS Workshop 10-11 October 2007 P. Colom & A.J. Boonstra 14
system design & spatial filtering
DOA, subspace techniques: order Nant3
Rank-one subspace techniques, single source DOA: order Nant2
If direction known, and apply to beamformer or correlator: cheap!
DOA and subspace estimation usually is expensiveespecially if it needs to be done at a high update rate
Applying fixed filters, known fixed directions• Most fixed transmitters: easily ~20 dB supp. • If propagation modifies spatial structure:
add closely spaced nulls / increase subspace to be removed• Very cheap method if combined with beamformer of correlator Applying on-line varying filters, moving interferers• Both for fixed and moving transmitters: good suppression• Filter distorts uvw data as well, but can be restored under certain conditions • Expensive method (online matrix operations)• Drawback: affects the beamshape => hampers on-line calibration, “smoothness
criterium”
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may be costlycan be used in combination with other methods
parametric techniques(assuming wide bands)
may be costly; changing sidelobes may impair calibration
somewhat better suppression than fixed; tracking possibilities
varying spatial filters, sidelobe canceller
lower SP load at output station beamformers
-[excision]
fluctuating beam may impair calibration
reduce strong RFI enables the use of less ADC bits / lessens LNA req.
varying spatial filtering, including sidelobe canceller
Antenna beam- formers (e.g. PAF)
difficult; needs careful calibrationreduce strong RFI enables the use of less ADC bits / lessens LNA req.
fixed spatial filtering
bookkeeping very costly; impairing gain estimate otherwise
low SP load unless booking is done on excised samples; fast transients
excision (assuming no subband filtering is done yet)
may be complex; may be time consuming
very flexible; can be added when necessary; relatively cheap
Spatial filtering,parametric techniques, …
Post processing
-can be done at short timescales and short bandwidths; common practice
excisionCorrelation
influences UVW data points;may impair calibration
may be applicable at shorter timescales than at location of correlator output
Interstation sidelobe cancelling/ spatial filtering, moving sources
Pre-correlation
more complex operation; connection wit cenral systems
very cheap; reduce data transport rate to central site
fixed spatial filterStation beamformers
Con’sPro’sMethodSignal path
Applicability in SKA
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