EXPERIMENTAL STUDY OF RADIO
FREQUENCY INTERFERENCE
DETECTION ALGORITHMS IN
MICROWAVE RADIOMETRY
José Miguel Tarongí Bauzá
Giuseppe Forte
Adriano Camps Carmona
RSLab
Universitat Politècnica de Catalunya
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
2
Introduction
-60
Power [dBm] (uncal)
Power [dBm] (uncal)
-50
-55
-60
-65
0
500
1000
1500
-62
-64
-66
-68
2.69
2000
2.692
2.694
2.696
2.698
Sample
Frequency [GHz]
Time
analysis
Frequency
analysis
2.7
Spectrogram
analysis
3
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
4
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.
5
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
Mexican Hat coefficient values
– RFI enhancement with the correlation of FWLD and TWLD with the
spectrogram
6
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 (σ pow)
which must be estimated
 WLD threshold (TWLD or FWLD):
Th WLD = Kσ WLD
with
σ WLD =
 c σ
5
i
pow
2N
i=1
 + c σ
2
6
pow
N

2
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
 Threshold selected to have a Pfa lower than 5·10-4
– 1st part of the algorithm can be performed several times.
7
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
8
Algorithm description
RFI cleaned
signal power
Spectrogram
FWLD
filter
*
FWLD
threshold
2D
Convolution
&
*
∑
TWLD
filter
2nd pass RFI
mitigation result
TWLD
threshold
1st pass RFI
mitigation result
Frequency subbands &
Time sweeps average
Frequency
threshold
Yes
&
Time
threshold
No
Any frequency subband or time sweep
with relatively high power (6 times
above σfreq or σtime) value?
9
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
– ImpulseRFI
Vertical lines
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
10
Results
TWLD filtering
and thresholding
FWLD filtering
and thresholding
11
Results
threshold
Time averaged spectrogram
threshold
Frequency averaged spectrogram
12
Results
2D wavelet
based filtering
Frequency and
time averaging
13
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
14
THANKS FOR YOUR ATTENTION