Performance analysis of a hardware implemented complex signal kurtosis radio-frequency interference detector

Schoenwald, A.; Bradley, Damon; Mohammed, Priscilla N.; Piepmeier, Jeffrey R.; Wong, Mark

doi:10.1109/microrad.2016.7530507

Cited by 10 publications

(8 citation statements)

References 4 publications

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“…The resulting correlation sequence is tested for RFI with the detection methods described above, ZCR and PCD. With regards to the reference metrics (Total Power and Kurtosis), they are obtained directly from the signal: signal power is obtained by combining the variance of both I, Q signals, and average signal kurtosis is computed as the mean of the kurtosis of each I, Q chains, a solution often implemented in practice [28]:…”

Section: Methodsmentioning

confidence: 99%

A Novel RFI Detection Method for Microwave Radiometers Using Multilag Correlators

Díez-García

Camps

2022

IEEE Trans. Geosci. Remote Sensing

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Radio Frequency Interference (RFI) is an increasing problem in Microwave Radiometry, particularly for Earth Observation, because the antennas are pointing towards the Earth. RFI has been observed at L-band in ESA's SMOS (Soil Moisture and Ocean Salinity) Earth Explorer mission, as well as in NASA's SMAP (Soil Moisture Active and Passive) and Aquarius missions, as well as in AMSR-E and WindSat missions at 10.7 GHz and 18.7 GHz [1]. Therefore, dedicated on-board systems are nowadays a must to detect and remove contaminated measurements, improving radiometric accuracy, and increasing the spatial coverage. In this work, a novel detection technique, specially tailored for Synthetic Aperture Interferometric Radiometers (SAIR), is proposed and its performance analysed. It is based on the change of the shape of the crosscorrelation function at lags different from zero under the presence of RFI. The performance of the proposed technique is compared to other common RFI detection algorithms, and its limitations and advantages are discussed. Post-correlation detection performance is found to out-perform other commonly used algorithms such as Kurtosis. In addition, it presents some convenient properties for its practical application in correlation and real aperture radiometers.

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Section: Methodsmentioning

confidence: 99%

A Novel RFI Detection Method for Microwave Radiometers Using Multilag Correlators

Díez-García

Camps

2022

IEEE Trans. Geosci. Remote Sensing

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“…This leads to the CSK (Complex Signal Kurtosis) RFI Test statistic [3,4,5] as shown in equation (12).…”

Section: Complex Signal Kurtosismentioning

confidence: 99%

“…While higher order test statistics prove effective at detecting pulsed interference [2], it is more difficult to detect continuous interference and complex modulations using the same Thanks to NASA Earth Science Technology Office NNH13ZDA001N ACT Funding statistical methods. To this end additional test statistics in previous work [3,4,5] were evaluated. This work now applies Independent Component Analysis (ICA) as a pre-processor for RFI detection.…”

Section: Introductionmentioning

confidence: 99%

RFI detection and mitigation using independent component analysis as a pre-processor

Schoenwald

Gholian

Bradley

et al. 2016

2016 Radio Frequency Interference (RFI)

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Radio-frequency interference (RFI) has negatively impacted scientific measurements of passive remote sensing satellites. This has been observed in the L-band radiometers Soil Moisture and Ocean Salinity (SMOS), Aquarius and more recently, Soil Moisture Active Passive (SMAP). RFI has also been observed at higher frequencies such as K band. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements. This work explores the use of Independent Component Analysis (ICA) as a blind source separation (BSS) technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

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“…RFI mitigation algorithms have been developed and used in existing satellite remote sensing missions such as Soil Moisture Active Passive (SMAP) [5,6]. While existing methods have been useful at flagging narrowband pulsed interference, research has been done to develop more sensitive detectors such as the complex signal kurtosis (CSK) detectors to efficiently flag wideband and continuous interference [7,8].…”

Section: Introductionmentioning

confidence: 99%

Radio frequency interference detection for passive remote sensing using eigenvalue analysis

Schoenwald

Kim

Mohammed

2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Radio frequency interference (RFI) can corrupt passive remote sensing measurements taken with microwave radiometers. With the increasingly utilized spectrum and the push for larger bandwidth radiometers, the likelihood of RFI contamination has grown significantly. In this work, an eigenvaluebased algorithm is developed to detect the presence of RFI and provide estimates of RFI-free radiation levels. Simulated tests show that the proposed detector outperforms conventional kurtosis-based RFI detectors in the low-to-medium interferece-to-noise-power-ratio (INR) regime under continuous wave (CW) and quadrature phase shift keying (QPSK) RFIs.

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Performance analysis of a hardware implemented complex signal kurtosis radio-frequency interference detector

Cited by 10 publications

References 4 publications

A Novel RFI Detection Method for Microwave Radiometers Using Multilag Correlators

A Novel RFI Detection Method for Microwave Radiometers Using Multilag Correlators

RFI detection and mitigation using independent component analysis as a pre-processor

Radio frequency interference detection for passive remote sensing using eigenvalue analysis

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