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

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Frequency [GHz]

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Time analysis

Frequency analysis

Spectrogram analysis

0 500 1000 1500 2000-65

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Sample

Po

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[d

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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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Results

TWLD filteringand thresholding

FWLD filteringand thresholding

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threshold

Time averaged spectrogram

Frequency averaged spectrogram

threshold

Results

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2D wavelet based filtering

Frequency and time averaging

Results

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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

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