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Radio Frequency Interference (RFI) in Microwave Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 51653

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Prof. Dr. Andrea Monti Guarnieri

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Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy
Interests: SAR; radar Interferometry; geosynchronous SAR; MIMO radar; radar constellations
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Dr. Paolo de Matthaeis

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NASA Goddard Space Flight Center, Code 615, Greenbelt, MD 20771, USA
Interests: microwave remote sensing; radio frequency interference; electromagnetic modeling of the earth surface

Prof. Dr. Adriano Camps

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CommSensLab – UPC, “María de Maeztu” Excellence Research Unit, Dept. of Signal Theory and Communications, Universitat Politècnica de Catalunya—BarcelonaTech (UPC) and Institut d’Estudis Espacials de Catalunya IEEC/CTE-UPC. UPC Campus Nord, building D4, office 016, c/Jordi Girona 31, 08034 Barcelona, Spain
Interests: microwave radiometry; GNSS-R; RFI mitigation; CubeSats; SMOS; soil moisture; sea surface salinty
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Special Issue Information

Dear Colleagues,

New technologies and consumer applications increasingly need to use radio frequencies. This is pushing demand for frequency spectrum to unprecedented levels, and as a result both active and passive spaceborne microwave remote sensing instruments are experiencing problems with the Radio Frequency Interference (RFI) more and more often. The presence of RFI is always detrimental to scientific missions. When detected, interference causes information loss and reduces measurement accuracy; while, when not detected, it leads to inaccurate measurements that are not recognized as such, and therefore to potentially wrong conclusions. RFI represents a significant threat to microwave remote sensing and will need proper attention in all future instrument planning and design.

RFI are strongly affecting microwave satellite borne missions, like radiometers, and Synthetic Aperture Radar, from LEO missions to GNSS and geosynchronous SAR sensors. The huge increase in WLAN and wireless devices, their expansion in C band, and the RFI generated by direct or backscattered signal, either in-band or by harmonics harms the present and future generation of spaceborne remote sensing.

This special issue will cover the different aspects of RFI, such as detection and mitigation of interference from different levels: from mission levels (swarms or distributed sensors), to a signal processing perspective and hardware design, encompassing both active and passive sensors. It also aims to include report of RFI observations, together with their trend, and to inform on the latest regulatory developments in spectrum management.

Prof. Andrea Monti Guarnieri
Dr. Paolo de Matthaeis
Prof. Adriano Camps
Guest Editors

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Keywords

Radio Frequency Interference
Microwave Remote Sensing
Active and passive radiometers
Geosynchronous SAR
GNSS

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Published Papers (10 papers)

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Research

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18 pages, 10634 KiB

Open AccessFeature PaperArticle

Detection of Residual “Hot Spots” in RFI-Filtered SMAP Data

by Yan Soldo, David Le Vine and Paolo de Matthaeis

Remote Sens. 2019, 11(24), 2935; https://doi.org/10.3390/rs11242935 - 7 Dec 2019

Cited by 7 | Viewed by 3137

Abstract

Radio frequency interference (RFI) is a well-documented problem for passive remote sensing of the Earth at L-band even though the measurements are made in the protected band at 1.413 GHz. Consequently, filtering for RFI is an important early step in the processing of measurements made by the SMAP (Soil Moisture Active/Passive) radiometer. However, the filtered data still include regions with suspiciously high antenna temperatures. One possible cause of these “hot spots” is interference not fully detected during RFI filtering. This paper presents evidence supporting this hypothesis and describes an algorithm to identify these “hot spots” so that they can be removed from the measurements. The impact of removing these “hot spots” is generally small, but evidence is presented that the brightness temperature and soil moisture improve when the hot spots are removed. Full article

(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)

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21 pages, 2845 KiB

Open AccessArticle

Impact of Signal Quantization on the Performance of RFI Mitigation Algorithms

by Raúl Díez-García and Adriano Camps

Remote Sens. 2019, 11(17), 2023; https://doi.org/10.3390/rs11172023 - 28 Aug 2019

Cited by 7 | Viewed by 3108

Abstract

Radio Frequency Interference (RFI) is currently a major problem in Communications and Earth Observation, but it is even more dramatic in Microwave Radiometry because of the low power levels of the received signals. Its impact has been attested in several Earth Observation missions. On-board mitigation systems are becoming a requirement to detect and remove affected measurements, increasing thus radiometric accuracy and spatial coverage. However, RFI mitigation methods have not been tested yet in the context of some particular radiometer topologies, which rely on the use of coarsely quantized streams of data. In this study, the impact of quantization and sampling in the performance of several known RFI mitigation algorithms is studied under different conditions. It will be demonstrated that in the presence of clipping, quantization changes fundamentally the time-frequency properties of the contaminated signal, strongly impairing the performance of most mitigation methods. Important design considerations are derived from this analysis that must be taken into account when defining the architecture of future instruments. In particular, the use of Automatic Gain Control (AGC) systems is proposed, and its limitations are discussed. Full article

(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)

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26 pages, 6737 KiB

Open AccessArticle

Interference Mitigation for Synthetic Aperture Radar Based on Deep Residual Network

by Weiwei Fan, Feng Zhou, Mingliang Tao, Xueru Bai, Pengshuai Rong, Shuang Yang and Tian Tian

Remote Sens. 2019, 11(14), 1654; https://doi.org/10.3390/rs11141654 - 11 Jul 2019

Cited by 65 | Viewed by 5410

Abstract

Radio Frequency Interference (RFI) is a key issue for Synthetic Aperture Radar (SAR) because it can seriously degrade the imaging quality, leading to the misinterpretation of the target scattering characteristics and hindering the subsequent image analysis. To address this issue, we present a narrow-band interference (NBI) and wide-band interference (WBI) mitigation algorithm based on deep residual network (ResNet). First, the short-time Fourier transform (STFT) is used to characterize the interference-corrupted echo in the time–frequency domain. Then, the interference detection model is built by a classical deep convolutional neural network (DCNN) framework to identify whether there is an interference component in the echo. Furthermore, the time–frequency feature of the target signal is extracted and reconstructed by utilizing the ResNet. Finally, the inverse time–frequency Fourier transform (ISTFT) is utilized to transform the time–frequency spectrum of the recovered signal into the time domain. The effectiveness of the interference mitigation algorithm is verified on the simulated and measured SAR data with strip mode and terrain observation by progressive scans (TOPS) mode. Moreover, in comparison with the notch filtering and the eigensubspace filtering, the proposed interference mitigation algorithm can improve the interference mitigation performance, while reducing the computation complexity. Full article

(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)

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43 pages, 18721 KiB

Open AccessArticle

Adaptive Antenna Pattern Notching of Interference in Synthetic Aperture Radar Data Using Digital Beamforming

by Tobias Bollian, Batuhan Osmanoglu, Rafael Rincon, Seung-Kuk Lee and Temilola Fatoyinbo

Remote Sens. 2019, 11(11), 1346; https://doi.org/10.3390/rs11111346 - 4 Jun 2019

Cited by 33 | Viewed by 5602

Abstract

Radio Frequency Interference (RFI) is a growing problem in Synthetic Aperture Radar (SAR) systems as scientific motivations push the radars to lower frequencies and as more wireless services share the frequency spectrum. New, advanced SAR instruments, such as NASA’s EcoSAR, DBSAR and DLR’s Tandem-L mission, employ a multichannel architecture capable of Digital Beamforming (DBF). Radars with DBF are capable of notching the antenna pattern in specific directions, which can be utilized to suppress RFI on board or in post-processing. A well-researched beamformer for this purpose is the Minimum Variance Distortionless Response (MVDR) Beamformer. However, the number of interferers that can be removed through notching is limited by the number of receive channels. It is therefore essential to adaptively change the antenna pattern notching throughout the image in time and frequency for the best results with a given number of receive channels. In this paper, we present several methods to achieve this notching by making use of the spatial SAR signal distribution in range time, range frequency, azimuth time and azimuth Doppler that is inherent to the SAR imaging geometry. Because this distribution is time-variable and yet predictable, it can be used to improve the angle of arrival estimation of the RFI and the adaptive notching. The presented methods can be applied to a Digital Beamforming (DBF) SAR signal with multiple channels in elevation and/or in azimuth. Simulations show that the proposed methods increase the ability to notch out-of-swath interference from multiple directions and lessen the impact on in-swath interference. The improvement of the interferometric coherence for a single-pass interferogram acquired by NASA’s EcoSAR system (capable of DBF in elevation) is demonstrated. The removal of periodic RFI artifacts is achieved. Full article

(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)

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11 pages, 9243 KiB

Open AccessArticle

Satellite Cross-Talk Impact Analysis in Airborne Interferometric Global Navigation Satellite System-Reflectometry with the Microwave Interferometric Reflectometer

by Raul Onrubia, Daniel Pascual, Hyuk Park, Adriano Camps, Christoph Rüdiger, Jeffrey P. Walker and Alessandra Monerris

Remote Sens. 2019, 11(9), 1120; https://doi.org/10.3390/rs11091120 - 10 May 2019

Cited by 16 | Viewed by 4157

Abstract

This work analyzes the satellite cross-talk observed by the microwave interferometric reflectometer (MIR), a new global navigation satellite system (GNSS) reflectometer, during an airborne field campaign in Victoria and New South Wales, Australia. MIR is a GNSS reflectometer with two 19-element, dual-band arrays, each of them having four steerable beams. The data collected during the experiment, the characterization of the arrays, and the global positioning system (GPS) and Galileo ephemeris were used to compute the expected delays and power levels of all incoming signals, and the probability of cross-talk was then evaluated. Despite the MIR highly directive arrays, the largest ever for a GNSS-R instrument, one of the flights was found to be contaminated by cross-talk almost half of the time at the L1/E1 frequency band, and all four flights were contaminated ∼5–10% of the time at the L5/E5a frequency band. The cross-talk introduces an error of up to 40 cm of standard deviation for altimetric applications and about 0.24 dB for scatterometric applications. Full article

(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)

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18 pages, 863 KiB

Open AccessArticle

Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

by Imke Hans, Martin Burgdorf and Stefan A. Buehler

Remote Sens. 2019, 11(7), 866; https://doi.org/10.3390/rs11070866 - 10 Apr 2019

Cited by 3 | Viewed by 4043

Abstract

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series. Full article

(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)

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19 pages, 5754 KiB

Open AccessArticle

Time-Varying SAR Interference Suppression Based on Delay-Doppler Iterative Decomposition Algorithm

by Jia Su, Haihong Tao, Mingliang Tao, Jian Xie, Yuexian Wang and Ling Wang

Remote Sens. 2018, 10(9), 1491; https://doi.org/10.3390/rs10091491 - 18 Sep 2018

Cited by 22 | Viewed by 3592

Abstract

Narrow-band interference (NBI) and Wide-band interference (WBI) are critical issues for synthetic aperture radar (SAR), which degrades the imaging quality severely. Since some complex signals can be modeled as linear frequency modulated (LFM) signals within a short time, LFM-WBI and NBI are mainly discussed in this paper. Due to its excellent energy concentration and useful properties (i.e., auto-terms pass through the origin of Delay-Doppler plane while cross-terms are away from it), a novel nonparametric interference suppression method using Delay-Doppler iterative decomposition algorithm is proposed. This algorithm consists of three stages. First, we present signal synthesis method (SSM) from ambiguity function (AF) and cross ambiguity function (CAF) based on the matrix rearrangement and eigenvalue decomposition. Compared with traditional SSM from Wigner distribution (WD), the proposed SSM can synthesize a signal faster and more accurately. Then, based on unique properties in Delay-Doppler domain, a mask algorithm is applied for interference identification and extraction using Radon and its inverse transformation. Finally, a signal iterative decomposition algorithm (IDA) is utilized to subtract the largest interference from the received signal one by one. After that, a well-focused SAR imagery is obtained by conventional imaging methods. The simulation and measured data results demonstrate that the proposed algorithm not only suppresses interference efficiently but also preserves the useful information as much as possible. Full article

(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)

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Review

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22 pages, 8836 KiB

Open AccessFeature PaperReview

A Review of RFI Mitigation Techniques in Microwave Radiometry

by J. Querol, A. Perez and A. Camps

Remote Sens. 2019, 11(24), 3042; https://doi.org/10.3390/rs11243042 - 17 Dec 2019

Cited by 33 | Viewed by 5561

Abstract

Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer. Full article

(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)

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24 pages, 14042 KiB

Open AccessReview

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

by Mingliang Tao, Jia Su, Yan Huang and Ling Wang

Remote Sens. 2019, 11(20), 2438; https://doi.org/10.3390/rs11202438 - 21 Oct 2019

Cited by 117 | Viewed by 9587

Abstract

Radio frequency interference (RFI) is a major issue in accurate remote sensing by a synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This paper provides a comprehensive study of the RFI mitigation techniques applicable for an SAR system. From the view of spectrum allocation, possible terrestrial and spaceborne RFI sources to SAR system and their geometry are analyzed. Typical signal models for various RFI types are provided, together with many illustrative examples from real measured data. Then, advanced signal processing techniques for removing RFI are reviewed. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends are provided from the perspective of cognitive, integrated, and adaptive. This review serves as a reference for future work on the implementation of the most suitable RFI mitigation scheme for an air-borne or space-borne SAR system. Full article

(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)

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Other

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11 pages, 4546 KiB

Open AccessFeature PaperTechnical Note

LEO to GEO-SAR Interferences: Modelling and Performance Evaluation

by Antonio Leanza, Marco Manzoni, Andrea Monti-Guarnieri and Marco di Clemente

Remote Sens. 2019, 11(14), 1720; https://doi.org/10.3390/rs11141720 - 20 Jul 2019

Cited by 12 | Viewed by 4552

Abstract

This paper proposes a statistical model to evaluate the impact of the signal backscattered by low Earth orbiting (LEO) synthetic aperture radar (SAR) and received by GEO-stationary orbiting SAR. The model properly accounts for the bistatic backscatter, the number of LEO-SAR satellites and their duty cycles. The presence of many sun-synchronous, dawn-dusk satellites creates a 24 h periodic pattern in interference that should be considered in the acquisition plan of future geostationary SAR. The model, implemented by a numerical simulator, allows also the prediction of performance in future scenarios of many LEO-SAR. Examples and evaluations are made here for X band. Full article

(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)

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