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Radio Frequency Interference (RFI) has long been a problem for L-band microwave radiometers, such as SMOS, Aquarius and SMAP. This paper reports on the activities performed by the SMAP RFI team to identify and report persistent sources with the aim of decreasing global RFI occurrences at L-band.
Aquarius has produced maps of salinity by measuring Earth’s natural emissions at L-band. However, measurements made by its instruments are affected by the presence of Radio Frequency Interference (RFI). For this reason, RFI detection algorithms had been implemented, both for the radiometer and the scatterometer, in order to reduce the impact of RFI on science data. In an effort to improve understanding of L-band RFI, the Aquarius mission has generated a new series of products. This contribution presents how these products were produced as well as the information that they contain. These products will be available starting at the end of January 2018 on the Aquarius website.
The Aquarius/SAC-D mission operated between August 2011 and June 2015 with the main goal of providing global estimates of sea surface salinity (SSS). It comprised both active and passive microwave sensors operating at L-band to observe the same surface area almost simultaneously. Measurements from both instruments underwent subsequent filtering to mitigate the effect of Radio Frequency Interference (RFI). This report describes the analysis of statistics of RFI in samples acquired by the Aquarius radiometers, and its results could be used to improve the performance of the interference detection algorithm.
The Soil Moisture Active Passive (SMAP) mission, launched in January 2015, provides global measurements of soil moisture using a microwave radiometer. SMAPs radiometer passband lies within the passive frequency allocation. However, both unauthorized in-band transmitters as well as out-of-band emissions from transmitters operating at frequencies adjacent to this allocated spectrum have been documented as sources of radio frequency interference (RFI) to the L-band radiometers on SMOS and Aquarius. The spectral environment consists of high RFI levels as well as significant occurrences of low level RFI equivalent to 0.1 to 10 K. The SMAP ground processor reports the antenna temperature both before and after RFI mitigation is applied. The difference between these quantities represents the detected RFI level. The presentation will review the SMAP RFI detection and mitigation procedure and discuss early on-orbit RFI measurements from the SMAP radiometer. Assessments of global RFI properties and source types will be provided, as well as the implications of these results for SMAP soil moisture measurements.
Microwave radiometers use radio spectrum dedicated to sensing the environment. As wireless communications and other active services proliferate, this allocated spectrum is nearly being crowded out. The potential result is corrupted satellite measurements of the weather, the climate, and the environment. We present an analog RFI detector for microwave radiometers intended to mitigate the above risks. The double detector (DD) for RFI detection includes a square-law diode detector with short integration time for measuring the total power out of the radiometer, followed by a second diode detector which acts as a higher-order statistical fourth-moment detector. See Figure 1 for block diagram of the system. This novel design which uses purely analog components at radio andlor intermediate frequencies allows the system to easily augment conventional radiometer architectures used in both airborne and space borne instruments. An equivalent high-speed digital design would add an impractical level of cost and complexity to radiometer designs using today's technology.
Here we developed a model for determining the effects of narrowband RFI on low resolution digital correlators. Low resolution correlators rely on a theoretical inversion to obtain the input correlation coefficient from the digital output. This inversion is based on the Gaussian statistics of the input signals. In the presence of narrow-band interference, the statistics are not Gaussian and the theoretical inversion is no longer valid. The result is an error in the correlator output. We studied this phenomena for four correlator resolutions: 1, 1.5, 2, and 4 bits. The errors are significant for 1, 1.5, and 2-bit systems. We found in the presence of relatively strong interference (INR approximately greater than 0 dB) the errors can be ten's of percent. The error reduces to less than 0.03% for INR less than -16 dB. For the four-bit correlators, the errors are less than 0.03% for all cases studied. The error is also nonlinearly dependent upon input correlation coefficient.
The presence of anthropogenic RFI is expected to adversely impact soil moisture measurement by NASA s Soil Moisture Active Passive mission. The digital signal processing approach and preliminary design for detecting and mitigating this RFI is presented in this paper. This approach is largely based upon the work of Johnson and Ruf.
A significant motivation for the development of a next generation system for geodetic VLBI was to address growing problems related to RFI. In this regard, the broadband 2-14 GHz frequency range proposed for VLBI2010 has advantages and disadvantages. It has the advantage of flexible allocation of band frequencies and hence the ability to avoid areas of the spectrum where RFI is worst. However, the receiver is at the same time vulnerable to saturation from RFI anywhere in the full 2-14 GHz range. The impacts of RFI on the VLBI2010 analog signal path, the sampler, and the digital signal processing are discussed. In addition, a number of specific RFI examples in the 2-14 GHz range are presented.
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Radio Frequency Interference (RFI) is a persistent and growing problem experienced by spaceborne microwave radiometers. Recent missions such as SMOS, SMAP, and GPM has detected RFI in L, C, X, and K bands. To proactively deal with this issue, microwave radiometers must (1) Utilize new algorithms for RFI detection (2) Utilize fast digital back-ends that sample at hundreds of MHz. The wideband digital signal processing testbed (WB-RFI) is a platform that allows rapid deelopment and testing various RFI detection and mitigation algorithms.
Radio Frequency Interference (RFI) is a persistent and growing problem experienced by spaceborne microwave radiometers. Recent missions such as SMOS, SMAP, and GPM have detected RFI in L, C, X, and K bands. To proactively deal with this issue, microwave radiometers must (1) Utilize new algorithms for RFI detection (2) Utilize fast digital back-ends that sample at hundreds of MHz. The wideband digital signal processing testbed (WB-RFI) is a platform that allows rapid development and testing various RFI detection and mitigation algorithms.
Radio frequency interference (RFI) is a risk for microwave radiometers due to their requirement of very high sensitivity. The Soil Moisture Active Passive (SMAP) mission has an aggressive approach to RFI detection and filtering using dedicated spaceflight hardware and ground processing software. As more sensors push to observe at larger bandwidths in unprotected or shared spectrum, RFI detection continues to be essential. This article presents a deep learning approach to RFI detection using SMAP spectrogram data as input images. The study utilizes the benefits of transfer learning to evaluate the viability of this method for RFI detection in microwave radiometers. The well-known pretrained convolutional neural networks, AlexNet, GoogleNet, and ResNet-101 were investigated. ResNet-101 provided the highest accuracy with respect to validation data (99%), while AlexNet exhibited the highest agreement with SMAP detection (92%).
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.
Anthropogenic Radio-Frequency Interference (RFI) drove both the SMAP (Soil Moisture Active Passive) microwave radiometer hardware and Level 1 science algorithm designs to use new technology and techniques for the first time on a spaceflight project. Care was taken to provide special features allowing the detection and removal of harmful interference in order to meet the error budget. Nonetheless, the project accepted a risk that RFI and its mitigation would exceed the 1.3-K error budget. Thus, RFI will likely remain a challenge afterwards due to its changing and uncertain nature. To address the challenge, we seek to answer the following questions: How does RFI evolve over the SMAP lifetime? What calibration error does the changing RFI environment cause? Can time series information be exploited to reduce these errors and improve calibration for all science products reliant upon SMAP radiometer data? In this talk, we address the first question.
An S-band radio frequency interference (RFI) receiver to analyze and identify sources of RFI problems in the Deep Space Network DSN tracking stations is described. The RFI receiver is a constant gain, double conversion, open loop receiver with dual sine/cosine channel outputs, providing a total of 20 MHZ monitoring capability. This receiver is computer controlled using a MODCOMP II miniprocessor. The RFI receiver has been designed to operate at a 150 Kelvin system noise temperature accomplished by cascading two low noise field effect transistor (FET) amplifiers for the receiver front-end. The first stage low noise FET amplifier is mounted at the feed horn to minimize any cable losses to achieve a lower system noise temperature. The receiver is tunable over the frequency range of 2150 to 2450 MHz in both sine/cosine output channels with a resolution of 100 kHz.
An evaluation of the radio frequency interference (RFI) conditions which would affect operating frequency band selections and data communications equipment design approaches for the Tracking Data Relay Satellite System (TDRSS) is presented. The subjects discussed are: (1) the scope of the investigation, (2) relative RFI in the frequency bands considered, (3) radar RFI power and duty factor, (4) radio relay communications RFI, (5) radio frequency band usage recommendations, and (6) radar RFI impact areas.
The planned Soil Moisture Active Passive (SMAP) mission will use both active radar and passive radiometer instruments at L-Band to measure and monitor both soilmoisture and freeze/thaw state globally. The frequency band allocated for the SMAP radar is shared with the Global Navigation Satellite Systems and ground-basedradiolocation services. Signals from those users present significant sources of anthropogenic radio frequency interference (RFI) which contaminate the radarmeasurements. To mitigate RFI, the radar is designed with tunable operating frequency, which allows the center frequency to be tuned to avoid RFI. The filtering scheme in the receiver is configured to get a high level of RFI suppression. To meet the high accuracy measurement requirements, RFI detection and correction will be required during ground data processing. Some candidate algorithms have been evaluated, and they have been tested against simulated SMAP data derived from the PALSAR data.