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RFI Mitigation Techniques for RadioAstronomy


									          RFI Mitigation Techniques for

Michael Kesteven
Australia Telescope National Facility
Groningen, 28 March 2010
•   Introduction
•   The issues
•   RFI-free environments
•   Blanking
•   RFI cancellation
•   The low RFI levels problem
•   The large dataset problem
•   Prospects

RFI mitigation. Groningen, 2010

In the past decade a number of RFI mitigation techniques have
  been trialled and shown to work.

Yet few observatories have on-line RFI mitigation installed.

Has its time now arrived, perhaps?

RFI mitigation. Groningen, 2010
There is no universal solution

• Different sources of RFI
       • TV/Communications
       • Satellites
       • Observatory-based
• Different types of Telescopes
       • Single dish
       • arrays
• Different observing regimes
       •   Low frequency; high frequency
       •   VLBI
       •   Pulsars
       •   Spectral line
       •   Continuum

RFI mitigation. Groningen, 2010
The ITU RA-769 argument

A.R.Thompson provided a useful framework to describe the
 impact of RFI. Of interest here is the link between the
 observation mode and the RFI levels.

Recognise that RFI entering the main beam of a telescope
 (LOFAR apart) is generally a lost cause. Pitch the debate at
 RFI in the far sidelobes - at the level corresponding to a 0 dBi

RFI mitigation. Groningen, 2010
Single Dish Operation

Natural defences are few
        antenna sidelobes (0 dBi gain)

Mitigation techniques work well
         adaptive filters

Datasets are modest (relatively speaking)
       detailed probes over the entire dataset are realistic

Vulnerable to low level interference

RFI mitigation. Groningen, 2010

Natural defences are better:
        antenna sidelobes
        phase tracking decorrelation
        delay decorrelation (continuum observations)
        spatial resolution

Mitigation techniques less well developed

Datasets could become huge
       Some advanced techniques may not be realistic in the
       near term

RFI mitigation. Groningen, 2010
Current response to RFI

- Flag/blank post-detection data.

- Retune the receiver to an adjacent frequency

- Tolerate it

- Reschedule the observations

RFI mitigation. Groningen, 2010
The Challenge

We need machinery to reduce the impact of RFI which is
 damaging the astronomer’s data.

- It should be automatic, reliable and robust.

- It should not introduce artefacts which mimic real results.

- The cost of applying the machinery should be predictable.

- The cost should be less than the cost of doing nothing.

   (cost : $, science, time)

RFI mitigation. Groningen, 2010
Mitigation Options

Pro-active mitigation : Avoidance
       Remove the RFI at source.

Re-active mitigation : Remove the RFI from the data
       Blank those parts of the astronomical data space which
        contain RFI ---- excision.

       Identify and remove the RFI while leaving the astronomy
         untouched --- cancellation.

RFI mitigation. Groningen, 2010
Avoidance                         no RFI to mitigate

- Remote Locations

- Regulation
       - Spectrum Management
       - Radio quiet zones

- Good observatory practice
       -   Discipline
       -   Good design
       -   Maintenance
       -   Constant monitoring

RFI mitigation. Groningen, 2010
RFI mitigation. Groningen, 2010
Blanking, Flagging

This is the current mitigation strategy of choice.

It is attractive to observers because it is simple and its
   consequences are predictable:

       • The loss in sensitivity is related to the amount of data discarded.
       • The effect on the image quality can be estimated.

       • It is straightforward in its implementation, and can easily be

RFI mitigation. Groningen, 2010

                                        Pulsed interferer (~msec)

                                      Radiometer integration period (~msec)


- The RFI events occupy a small fraction of the data space.
- Each RFI event has to be detectable – needs INR > 1 in small number of samples.

    RFI mitigation. Groningen, 2010
Excision – Real Time blanking

- Given the mean and rms of good data, define RFI to be those
  samples above a threshold (= r*rms)

- Need to buffer some small number of data samples in order to
  be able to distinguish good from bad.

- The buffer allows you to apply some intelligence: determine the
  local mean and rms in order to identify the outliers.

- The buffer allows further options – one could blank a known
  pulse shape, for example.

RFI mitigation. Groningen, 2010

A number of observatories have built hardware, on-line blanking

- Arecibo, for example, addresses the serious RFI from
  neighbouring radar. The known timing details of the pulsing
  assists the blanking trigger.

- WSRT have demonstrated an impressive unit built around
  digital processing boards which, amongst in many capabilities,
  can provide on-line blanking.

RFI mitigation. Groningen, 2010
Excision                          Post-correlation Blanking.

Flag the data – instruct the downstream imaging/processing
 machinery to ignore the corrupt samples.

This is the RFI-mitigation strategy of last resort.

Tedious when done manually; automated scripts now available.

When this is applied to the correlator output data, the minimum
 quantum of rejected data is the size of the correlator dump

RFI mitigation. Groningen, 2010
ATCA – Middleberg
                                  Automated flagging

RFI mitigation. Groningen, 2010
Frequency Blanking

Discarding data in frequency space is a variant of this approach:
 modern high speed processing allows fine on-line spectral
 analysis, so that corrupted channels can be identified and

This is an option if the discarded fraction of frequency space is
 modest compared to the overall bandwidth.

LOFAR includes this in its armoury.

RFI mitigation. Groningen, 2010
Excision Issues

The technique relies on the ability to detect RFI from a small
 number of samples (or from a priori information). It generally
 requires good INR. Long integrations with low INR will be

INR > 10 is a rough guide. There may be little to be gained by
  integration if the RFI is pulsed, as the INR is essentially based
  on a relatively small number of samples. (Periodic RFI is a
  separate case).

Downstream processing should not be compromised. Care
 needed in defining the replacement sample.

Discarding data in synthesis arrays will affect the (u,v) plane
 population and may therefore compromise the imaging quality.

RFI mitigation. Groningen, 2010
Excision – Bottom line

It can be a viable technique if the cost to science is modest.

It depends on some prior definition of “badness”, and it depends
   on a low duty cycle.

RFI mitigation. Groningen, 2010

This is the more ambitious approach – identify and characterise
   the RFI; then remove just the RFI.

This is a two-step process:

1. Characterise the RFI.

2. Subtract the RFI from the data – to give the astronomer an
   RFI-free dataset.

RFI mitigation. Groningen, 2010
Cancellation                      -- How is the RFI identified?

       - Extract the RFI details from the data itself.

       - Point a reference antenna towards the RFI -- use
       adaptive filter.

       - Predict the RFI from published data (eg, GLONASS) –
       use software adaptive filter.

RFI mitigation. Groningen, 2010

Mitigate on the data on-the-fly ? (each correlator dump)


Mitigate on the entire observation.

SNR is the issue with the first; Data volume the problem with the

RFI mitigation. Groningen, 2010
Filter Variants

• Image plane filtering

• Spatial filtering

• Null Steering

• Cyclo-stationary filters

• Adaptive filters

RFI mitigation. Groningen, 2010
Clean/self-calibration filter            (Cornwell-NRAO)

Identify the RFI in the imaging stage. Apply self-calibration to the
  RFI. Remove the RFI.

Stationary RFI will map to the pole.

The self-calibration operates simultaneously in two areas :
 - The astronomical target;
 - The RFI which is stationary with respect to the observatory.
 The self-calibration accounts for the phasing and amplitude

It requires the data to be sampled much faster than the
   astronomical target would require.

RFI mitigation. Groningen, 2010
   Cornwell – 327 MHz.

        No Filtering              Filter active

RFI mitigation. Groningen, 2010
Spatial Filtering

• Each object within the field of view of the array will add a
  specific signature to the full set of correlation products between
  the antennas.

• An eigenvalue decomposition of the product matrix will isolate
  the strongest sources.

• A projection operation can then remove the RFI sources.

• This scheme has long history, most recently successfully
  demonstrated in the LOFAR trials.

RFI mitigation. Groningen, 2010
The LOFAR snapshot variant

1. Within each widefield (whole sky) snapshot identify and
   remove the RFI point sources (as found by spatial filtering).

        This cleans the snapshot down to sky noise.

2. Stacking the sky-aligned snapshots builds the SNR on the
   astronomical objects while dissolving the remaining RFI.

RFI mitigation. Groningen, 2010
This scheme is best suited to low frequency arrays (LOFAR)

There are problems at higher frequencies, where very short
 correlator cycle times are required.

The computing load for a detailed spatial filtering operation may
 be a limiting factor.

RFI mitigation. Groningen, 2010
Cyclostationary Filters

• The concept here is to identify the RFI by its temporal
  signature, cyclostationarity. This attribute is specific to RFI.

• The classical spatial filtering matrix is replaced by a variant
  which is matched to a cyclic frequency.

• The projection operation then proceeds as before, to remove
  the RFI.

• This scheme has had some initial (promising) trials on LOFAR.

RFI mitigation. Groningen, 2010
Null Steering

The ATA is an array of 42 antennas that includes a beamformer
 mode of operation, each beam directed to a potential target.
 This opens the possibility of adjusting the beamformer weights
 to position nulls in the direction of known RFI sources – fixed or

Wide-band nulls may be required (and have been demonstrated).

The process works well, but has serious implications for the
 bandwidth of the phase tracking machinery.

RFI mitigation. Groningen, 2010
Adaptive Filters

These have been applied to :

• Single Dish
• Arrays

The starting point is to obtain a copy of the RFI.

We manipulate this copy to match the RFI in the data – the
 function of the adaptive filter.

We then subtract the modified copy from the data.

RFI mitigation. Groningen, 2010
Cancellation                      Real-time adaptive filter

RFI mitigation. Groningen, 2010
Cancellation.                     Issues with the real-time filter

It requires modest INR. Averaging at the correlation step helps.

It can cope with multi-pathing, but not with multiple transmitters
   on the same frequency channel.

With no RFI there is no added noise. Gain drops to zero.

It adapts automatically to changes in the relative transmission
   path details.

RFI mitigation. Groningen, 2010
               Filter OFF         Filter ON

RFI mitigation. Groningen, 2010
Real-time adaptive filter

Best suited to continuum single dish observations.

Works well for pulsar and VLBI observations.

It may not be suitable for spectral line observations, as the
   cancellation is not complete, and the residuals will mimic the
   original RFI spectrum.

It could be difficult to implement in an array.

RFI mitigation. Groningen, 2010
Post-Correlation adaptive filter

• We combine three cross-products to get a good estimate of the
  interference in the astronomical channel.

• No total power products in the cross-products, thus no bias.

• Noise*RFI products are also removed.

• The signal/noise is set by the ratio of Correlated RFI to noise
  products -

                                  SNR ~ INR B c

RFI mitigation. Groningen, 2010
          Single Dish – post-correlation

Reference antenna

RFI mitigation. Groningen, 2010
                                  Parkes 64m
Adaptive filters

• Both real-time and post-correlation filters use the INR as a
  control factor – the filter switches off when INR <~ 1
  This makes the filter robust.

• Both filters subtract the correction term from the raw astronomy
  signal – they do not modify the astronomy.

• The real-time filter provides attenuation, leaving some residual
  RFI power;

• The post-correlation provides cancellation, with some added
  residual zero-mean noise.

RFI mitigation. Groningen, 2010
Postcorrelation filter applied to an array

• The Post-correlation adaptive filter has been successfully
  applied to an array.

• A reference antenna provided the RFI copy.

• This copy followed the same conversion chain and correlation
  path as all the antennas of the array.

• Each baseline was corrected for RFI after computing a
  baseline-specific correction term.

RFI mitigation. Groningen, 2010
Synthesis Array Filtering
(ATCA, 1503 MHZ, 4 MHZ BW)

RFI mitigation. Groningen, 2010
Before and after images

RFI mitigation. Groningen, 2010
Post-correlation Adaptive filter - Issues

- Still effective with low INR (to ~ 0.1)

- Will require additional correlator capacity

- Works well with single dish.

- Works well with an array, but may require short correlator
  dump times

RFI mitigation. Groningen, 2010
The Low INR problem

The mitigation schemes generally work on short sections of data,
but the astronomer works with the entire dataset.

Low level RFI which may only show up in the final product is of

Future arrays may have too much data to allow RFI mitigation
 predicated on the entire dataset. The ASKAP, for example,
 needs to complete the processing on-the-fly, when in high-
 resolution spectral mode.

RFI mitigation. Groningen, 2010

The prospects look good at the low-frequency (LOFAR) end of
 the spectrum.

The issue is less clear at SKA frequencies and above.

A number of niche areas, such as VLBI and pulsars, look

RFI mitigation. Groningen, 2010
Australia Telescope National Facility
Michael Kesteven

Phone: 61 2 9372 4544

Thank you
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