EXPERIMENTAL STUDY OF RADIO FREQUENCY INTERFERENCE DETECTION ALGORITHMS IN MICROWAVE RADIOMETRY
José Miguel Tarongí BauzáGiuseppe Forte
Adriano Camps CarmonaRSLab
Universitat Politècnica de Catalunya
2
Introduction
Radio Frequency interference (RFI) present in radiometric measurements lead to erroneous retrieval of physical parameters.
Several RFI mitigation methods developed:– Time analysis
– Frequency analysis
– Statistical analysis
– Time-Frequency (T-F) analysis Short Time Fourier Transform (STFT) [1] Wavelets [2]
STFT combines information in T-F, useful if frequency components vary over time.
Spectrogram → image representation of the STFT. Image processing tools can detect RFI present in a spectrogram.[1]. Tarongi, J. M ; Camps, A.; “Radio Frequency Interference Detection Algorithm Based on Spectrogram Analysis”, IGARSS 2010, 2010, 2, 2499-2502.
[2] Camps, A.; Tarongí, J.M.; RFI Mitigation in Microwave Radiometry Using Wavelets. Algorithms 2009, 2, 1248-1262. c
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2.69 2.692 2.694 2.696 2.698 2.7-68
-66
-64
-62
-60
Frequency [GHz]
Po
wer
[d
Bm
] (u
nca
l)
Time analysis
Frequency analysis
Spectrogram analysis
0 500 1000 1500 2000-65
-60
-55
-50
Sample
Po
wer
[d
Bm
] (u
nca
l)Introduction
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Hardware Settings
RFI detector hardware– Microwave radiometer based on a spectrum analyzer architecture– Composed by:
L-band horn antenna: Γ ≤ -17dB @ 1.4 – 1.427GHz Chain of low noise amplifiers: 45dB Gain and 1.7dB Noise figure Spectrum analyzer able to perform Spectrograms
– Calibration and temperature control unnecessary Only used to detect RFI
– Measurements taken in the Remote Sensing lab from the UPC
RFI detector Schematic
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Algorithm description
Objective ―> Image processing tools applied to the spectrogram to detect RFI.
1st idea: use algorithms previously developed [1]– Pixels conforming the spectrogram obtained by the spectrum analyzer
have a Raileigh distribution
– Frequency response of the RFI detector hardware was not sufficiently flat
New algorithm developed– 2D wavelet-based filtering to detect most part of the RFI
– Frequency and time averaging to eliminate the residual RFI
[1]. Tarongi, J. M ; Camps, A.; “Radio Frequency Interference Detection Algorithm Based on Spectrogram Analysis”, IGARSS 2010, 2010, 2, 2499-2502.
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Algorithm description
1st part, 2D wavelet based filtering– Convolution with two Wavelet Line Detection (WLD) filters
– WLD filters: matrixes based on a Mexican hat wavelet
– Two different filters: Frequency WLD (FWLD): detects sinusoidal RFI. Time WLD (TWLD): detects impulse RFI.
– Values of these filters: FWLD: TR rows (15 ≤ TR ≤ 31), each one composed by the coefficient values of a Mexican hat wavelet of 11 samples TWLD: TC columns (15 ≤ TC ≤ 31), each one composed by the coefficient values of a Mexican hat wavelet of 11 samples
– RFI enhancement with the correlation of FWLD and TWLD with the spectrogram
Mexican Hat coefficient values
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Algorithm description
1st part, 2D wavelet based filtering– Threshold to discriminate RFI in both filtered spectrograms:
Function of the standard deviation of the RFI-free noise power ( ) which must be estimated
WLD threshold (TWLD or FWLD):
Threshold selected to have a Pfa lower than 5·10-4
– 1st part of the algorithm can be performed several times.
powσ
WLD WLD Th = σK
with
5 2 2
WLD i pow 6 pow
i=1
σ = c σ 2N + c σ N
K = constant to determine the Pfa
ci = ith coefficient of the mexican hat wavelet (11 samples)N = # of rows/cols of the FWDL/TWDL filtered spectrogram
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Algorithm description
2nd part, frequency and time averaging– After 2D wavelet filtering it still remains residual RFI, next pass:
Average of the frequency subbands Average of the time sweeps
– Spectrogram matrix is converted in two vectors.
– RFI is eliminated with threshold proportional to the standard deviation of both vectors
– Threshold selected to have a Pfa lower than 5·10-3
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Algorithm description
Spectrogram
*TWLDfilter*
2D Convolution
FWLDfilter
FWLDthreshold
TWLDthreshold&
1st pass RFI mitigation result
Frequency subbands & Time sweeps average
Yes
Frequencythreshold
2nd pass RFI mitigation result
RFI cleaned signal power
∑
Any frequency subband or time sweep with relatively high power (6 times
above σfreq or σtime) value?
Timethreshold&
No
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Results
Measurements performed at the UPC (D3-213 bldg) L-band (1.414 - 1.416 GHz) Continuous sinusoidal wave and impulsional RFI detected:
– Sinusoidal RFI Vertical lines
– ImpulseRFI Horizontal lines
Spectrogram of a radiometric signal in the "protected" 1.400 - 1.427 MHz band with clear RFI contaminated pixels.
– Vertical line: CW RFI at 1415.4 MHz– Horizontal line: Impulsional RFI at 36 s
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Conclusions
Best RFI algorithm is actually a combination of:– 2D image filtering of the spectrogram using line detection filters.
– Time and frequency analysis to the remaining radiometric signal
System equalization may be performed:– Avoid false alarms from the RFI detection algorithm
– Let the application of other RFI detection algorithms