2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 1 Outline -progress in RFI mitigation (methods inventory) -system design & RFI mitigation: what and where System design & RFI mitigation DS4T3 (OPAR, ASTRON, INAF-IRA, UORL, CSIRO)

2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 2 Deliverable Achieved 1RFI mitigation methods inventoryfall Influence on data qualityY3Q4 3Impact of moving interference sourcesfall Cost effect. and tech. Requirements SKA siteY3Q4 5Demonstrations with EMBRACE, BEST …Y4Q4 6RFI mitigation strategies for the SKA Y4Q4 phased-arrays System design & RFI mitigation DS4T3 (OPAR, ASTRON, INAF-IRA, UORL, CSIRO)

2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 3 RFI Mitigation Methods Inventory Report, June 2007 Introduction Spectral selectivity Temporal selectivity Spatial Selectivity Multi-dimensional techniques Implications for SKA and conclusions

2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 4 Spectral selectivity polyphase filterbank ALMA memo 447 (J. Bunton) for cascaded PFB

2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 5 Spectral selectivity narrow band RFI elimination

2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 6 Temporal selectivity Blanking Detection theory based on hypothesis testing

2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 7 Temporal selectivity Blanking Single-antenna detection: Pfa and P D are known Pfa = Q   (2  P D = 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 P D = Q   (2  / (1+p.INR)) residual after blanking : INR res  1 / p.N 1/2 N: number of samples

2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 8 Spatial selectivity Filtering Algorithms are based on modifications of data covariance matrix by a spatial filter, such that: P k a k = 0 (a k direction of interferer) P k applied to covariance matrix: interferer energy nulled when a k 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 :

2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 9 Multi-dimensional techniques Cyclostationarity cyclostationary process : statistics are periodic with time Random binary signal: temporal view covariance : time origin as random covariance : time origin as constant

2 nd SKADS Workshop October 2007P. 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

2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 11 system design & RFI mitigation data 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)

2 nd SKADS Workshop October 2007P. 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

2 nd SKADS Workshop October 2007P. 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

2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 14 system design & spatial filtering DOA, subspace techniques: order N ant 3 Rank-one subspace techniques, single source DOA: order N ant 2 If direction known, and apply to beamformer or correlator: cheap! DOA and subspace estimation usually is expensive especially 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”

2 nd SKADS Workshop October 2007P. Colom & A.J. Boonstra 15 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