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EXPERIMENTAL STUDY OF RADIO FREQUENCY INTERFERENCE DETECTION A LGORITHMS IN MICROWAVE RADIOMETRYPowerPoint Presentation

EXPERIMENTAL STUDY OF RADIO FREQUENCY INTERFERENCE DETECTION A LGORITHMS IN MICROWAVE RADIOMETRY

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### 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. Algorithms2009, 2, 1248-1262. c

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

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.

- 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

- FWLD: TR rows (15 ≤ TR ≤ 31), each
- RFI enhancement with the correlation of FWLD and TWLD with the spectrogram

Mexican Hat coefficient values

- 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.

- Threshold to discriminate RFI in both filtered spectrograms:

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

with

- 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

- After 2D wavelet filtering it still remains residual RFI, next pass:

RFI cleaned

signal power

Spectrogram

FWLD

filter

TWLD

filter

2D Convolution

∑

*

*

2nd pass RFI mitigation result

FWLD

threshold

TWLD

threshold

&

Frequency

threshold

Time

threshold

&

1st pass RFI mitigation result

Yes

No

Any frequency subband or time sweep with relatively high power (6 times above σfreq or σtime) value?

Frequency subbands & Time sweeps average

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

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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