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Radio Frequency Interference Mitigation Algorithms – Design, Implementation and
Validation using Matlab-Simulink
Kaushal D. BuchDigital Backend Group,
Giant Metrewave Radio Telescope, NCRA-TIFR,
Khodad, Dist. Pune
Agenda
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Introduction
• Radio Telescopes
• Characteristics of radio astronomy signal
• Radio Frequency Interference (RFI) - Sources and Characteristics
• Development Cycle
RFI mitigation algorithm and its implementation using Matlab
• Data Analysis
• Time and Spectral domain RFI filtering
RFI mitigation algorithm and its implementation using Simulink
• Design and Implementation using Simulink and Xilinx System Generator
• Functional Verification
• Validation
Conclusion
Radio Telescopes
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Radio Telescopes:
• Highly sensitive radio receivers for analyzing the radio emissions from outer
space
• Conventionally employ a large parabolic reflector or an array of reflectors for
achieving desired sensitivity and angular resolution.
• Typical real-time compute capability of the order of ~102 - 103 Gflops. Use of
FPGA, ASIC, GPU for digital signal processing.
Giant Metrewave Radio Telescope:
• Located ~ 90 km north of Pune
• Array of 30# of 45 m diameter prime focal reflectors operating in the region of
150 – 1500 MHz spread over a Y-shape with 12 km radius
• One of the most sensitive radio telescopes in the world at metre wavelengths
Radio Frequency Interference (RFI)
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Man-made unintentional radiation from electronic/electrical equipments.
RFI has much higher power level and hence severely affects faint astronomical signal
RFI mitigation – very important problem (challenge) for contemporary radio telescopes
Statistically, RFI differs from the astronomical signal as it does not possess Gaussian distribution.
Digitized time series with impulsive RFI
Magnitude Spectrum of
Narrowband RFI
RFI
RFI
Signal
Typical Sources of RFI
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Image Courtesy: WikipediaSparking
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RFI Mitigation Algorithms
Data Analysis
Design
SimulationImpleme-
ntation
Validation
Development Cycle
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Data Analysis: Statistical Properties
Use of Matlab, Statistics Toolbox, Signal Processing Toolbox, Plotting tools in
data analysis to understand the statistical and spectral properties of RFI.
Matlab is extensively used for non-normality detection, outlier detection and
analysis of power spectrum of signal to understand the effects of RFI.
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Binned Data
Underlying Distribution
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Narrowband RFI
Analysis using SPTool on digitized time series
Time Series (Signal + RFI)
Data Analysis: Spectral Properties
Signal
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Data Analysis: Spectral Properties
Analysis of integrated spectrogram at the output of telescope’s signal processing chain
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Time domain RFI Mitigation
Use of Matlab, Statistics Toolbox, Signal Processing Toolbox, Plotting tools for
understanding properties of statistical estimator, threshold computation and
trade-off analysis
Non-linear filtering of time-series with RFI (blue) using robust statistical
estimator – filtered time series (red)
RFI
Signal
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Spectral Domain RFI Mitigation
Use of Matlab, Statistics Toolbox, Signal Processing Toolbox, Plotting tools
Spectrogram post RFI mitigationSpectrogram of noise with narrowband RFI RFI
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Simulation / Implementation using Simulink
Use of Matlab, Simulink, Xilinx System Generator, Signal Processing Toolbox,
Fixed Point Toolbox, Statistics Toolbox, Plotting tools
RFI mitigation algorithm was implementedusing Xilinx System Generator block setfollowing a model based design approachintegrated within the Matlab-Simulinkenvironment
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RFI Mitigation Block
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Functional Verification of RFI Mitigation Algorithms
RFI with different types of RFI was emulated using Matlab. This is
required for validation of RFI mitigation algorithms.
Example shows impulsive RFI of varying degree used as a test-bench
for quantifying the efficacy of the RFI algorithm.
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Model based design using Simulink and Xilinx
System Generator
The data acquired from the FPGA is imported in
Matlab for comparison with the theoretical
performance.
This development is now a part of the digital signal
processing system at the GMRT for real-time
removal of radio frequency interference.
Validation of RFI algorithms on FPGA
Conclusion
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Development and Implementation of RFI mitigation
techniques is an interdisciplinary area involving data
analysis, statistics, signal processing and digital design.
Matlab & Simulink, owing to their vast range of toolboxes
and diverse capabilities were useful in designing,
simulating, benchmarking and validating RFI mitigation
algorithms.
As more sophisticated algorithms are under development,
the functionality of Matlab’s Parallel Computing Toolbox is
being explored for simulation and analysis of large data sets
required for this application.
THANK YOU
One of the 30 GMRT antennas receiving radio waves from distant parts of the universe
