An introduction to factor analysis for radio frequency interference detection on satellite observations

A novel radio frequency interference (RFI) detection method is introduced for satellite-borne passive microwave radiometer observations. This method is based on factor analysis, in which variability among observed and correlated variables is described in terms of factors. In the present study, this method is applied to the Tropical Rainfall Measuring Mission (TRMM)/TRMM Microwave Imager (TMI) and Aqua/Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) satellite measurements over the land surface to detect the RFI signals, respectively, in 10 and 6 GHz channels. The RFI detection results are compared with other traditional methods, such as spectral difference method and principal component analysis (PCA) method. It has been found that the newly proposed method is able to detect RFI signals in the C- and X-band radiometer channels as effectively as the conventional PCA method

Manifold-Based System for Passive-Active Spectrum Sharing

A feasibility study for an automated scheme for spectrum sharing between passive and active users is presented. The needs of spectrum users can be represented by manifolds in a Euclidean hyperspace called electrospace which has 7 dimensions: frequency (f); Cartesian coordinates (x,y,z); angular coordinates (θ,ø), and time (t). The entire globe is tessellated into geographical areas containing spectrum users, called user domains. Each user domain is recursively tessellated into smaller user domains, or subdomains. A computer cluster, or broker, in each smallest subdomain performs the calculations necessary to determine if a particular user in the subdomain experiences interference. Throughout this thesis, the Chicago Loop (area of 4.09 km2, population ~ 21, 000) is taken to be the representative example of a smallest subdomain. Within each subdomain, the number of users served by a broker is reduced to a manageable number by the process of culling. There are three orders of culling. In first-order culling, subdomain pairs without line of sight and not close enough to mutually interfere are culled, or removed from further consideration for interference calculations. In second-order culling, within each subdomain an intersection test of the electrospace manifolds of all user pairs is performed. User pairs whose manifolds do not intersect are culled. In third-order culling a Friis calculation is performed for all remaining user pairs. The output of third-order culling is an RFI flag bit for each user indicating whether interference is present or not. The computational complexity of first-, second-, and third-order culling calculations was determined. Three representative user classes will be discussed: WiFi access points, Terminal Doppler Weather Radars, and passive EESS satellites. The manifold descriptor language (MDL) for each of the three user classes was described. The computational complexity of broker calculations to determine electrospace parameters from the MDL was determined. Using this complexity and the complexity of culling calculations, the total computational requirements for a broker in a representative subdomain is determined in GFLOPS (Giga Floating Point Operations Per Second)

Real-Time Detection and Filtering of Radio Frequency Interference On-board a Spaceborne Microwave Radiometer: The CubeRRT Mission

The Cubesat Radiometer Radio frequency interference Technology validation mission (CubeRRT) was developed to demonstrate real-time on-board detection and filtering of radio frequency interference (RFI) for wide bandwidth microwave radiometers. CubeRRT’s key technology is its radiometer digital backend (RDB) that is capable of measuring an instantaneous bandwidth of 1 GHz and of filtering the input signal into an estimated total power with and without RFI contributions. CubeRRT’s on-board RFI processing capability dramatically reduces the volume of data that must be downlinked to the ground and eliminates the need for ground-based RFI processing. RFI detection is performed by resolving the input bandwidth into 128 frequency sub-channels, with the kurtosis of each sub-channel and the variations in power across frequency used to detect non-thermal contributions. RFI filtering is performed by removing corrupted frequency sub-channels prior to the computation of the total channel power. The 1 GHz bandwidth input signals processed by the RDB are obtained from the payload’s antenna (ANT) and radiometer front end (RFE) subsystems that are capable of tuning across RF center frequencies from 6 to 40 GHz. The CubeRRT payload was installed into a 6U spacecraft bus provided by Blue Canyon Technologies that provides spacecraft power, communications, data management, and navigation functions. The design, development, integration and test, and on-orbit operations of CubeRRT are described in this paper. The spacecraft was delivered on March 22nd, 2018 for launch to the International Space Station (ISS) on May 21st, 2018. Since its deployment from the ISS on July 13th, 2018, the CubeRRT RDB has completed more than 5000 hours of operation successfully, validating its robustness as an RFI processor. Although CubeRRT’s RFE subsystem ceased operating on September 8th, 2018, causing the RDB input thereafter to consist only of internally generated noise, CubeRRT’s key RDB technology continues to operate without issue and has demonstrated its capabilities as a valuable subsystem for future radiometry missions

IMPROVED SATELLITE MICROWAVE RETRIEVALS AND THEIR INCORPORATION INTO A SIMPLIFIED 4D-VAR VORTEX INITIALIZATION USING ADJOINT TECHNIQUES

Microwave instruments provide unique radiance measurements for observing surface properties and vertical atmosphere profiles in almost all weather conditions except for heavy precipitation. The Advanced Microwave Scanning Radiometer 2 (AMSR2) observes radiation emitted by Earth at window channels, which helps to retrieve surface and column integrated geophysical variables. However, observations at some X- and K-band channels are susceptible to interference by television signals transmitted from geostationary satellites when AMSR2 is scanning regions including the U.S. and Europe, which is referred to as Television Frequency Interference (TFI). It is found that high reflectivity over the ocean surface is favorable for the television signals to be reflected back to space. When the angle between the Earth scene vector and the reflected signal vector is small enough, the reflected TV signals will enter AMSR2’s antenna. As a consequence, TFI will introduce erroneous information to retrieved geophysical products if not detected. This study proposes a TFI correction algorithm for observations over ocean. Microwave imagers are mostly for observing surface or column-integrated properties. In order to have vertical temperature profiles of the atmosphere, a study focusing on the Advanced Technology Microwave Sounder (ATMS) is included. A traditional AMSU-A temperature retrieval algorithm is modified to remove the scan biases in the temperature retrieval and to include only those ATMS sounding channels that are correlated with the atmospheric temperatures on the pressure level of the retrieval. The warm core structures derived for Hurricane Sandy when it moved from the tropics to the mid-latitudes are examined. Significant improvements have been obtained for the forecasts of hurricane track, but not intensity, especially during the first 6-12 hours. In this study, a simplified four-dimensional variational (4D-Var) vortex initialization model is developed to assimilate the geophysical products retrieved from the observations of both microwave imagers and microwave temperature sounders. The goal is to generate more realistic initial vortices than the bogus vortices currently incorporated in the Hurricane Weather Research and Forecasting (HWRF) model in order to improve hurricane intensity forecasts. The case included in this study is Hurricane Gaston (2016). The numerical results show that the satellite geophysical products have a desirable impact on the structure of the initialized vortex

Real-Time RFI Mitigation for the Apertif Radio Transient System

Current and upcoming radio telescopes are being designed with increasing sensitivity to detect new and mysterious radio sources of astrophysical origin. While this increased sensitivity improves the likelihood of discoveries, it also makes these instruments more susceptible to the deleterious effects of Radio Frequency Interference (RFI). The challenge posed by RFI is exacerbated by the high data-rates achieved by modern radio telescopes, which require real-time processing to keep up with the data. Furthermore, the high data-rates do not allow for permanent storage of observations at high resolution. Offline RFI mitigation is therefore not possible anymore. The real-time requirement makes RFI mitigation even more challenging because, on one side, the techniques used for mitigation need to be fast and simple, and on the other side they also need to be robust enough to cope with just a partial view of the data. The Apertif Radio Transient System (ARTS) is the real-time, time-domain, transient detection instrument of the Westerbork Synthesis Radio Telescope (WSRT), processing 73 Gb of data per second. Even with a deep learning classifier, the ARTS pipeline requires state-of-the-art real-time RFI mitigation to reduce the number of false-positive detections. Our solution to this challenge is RFIm, a high-performance, open-source, tuned, and extensible RFI mitigation library. The goal of this library is to provide users with RFI mitigation routines that are designed to run in real-time on many-core accelerators, such as Graphics Processing Units, and that can be highly-tuned to achieve code and performance portability to different hardware platforms and scientific use-cases. Results on the ARTS show that we can achieve real-time RFI mitigation, with a minimal impact on the total execution time of the search pipeline, and considerably reduce the number of false-positives.Comment: 6 pages, 10 figures. To appear in Proceedings from the 2019 Radio Frequency Interference workshop (RFI 2019), Toulouse, France (23-26 September

A Demonstration of the Effects of Digitization on the Calculation of Kurtosis for the Detection of RFI in Microwave Radiometry

Abstract-Microwave radiometers detecting geophysical parameters are very susceptible to radio-frequency interference (RFI) from anthropogenic sources. RFI is always additive to a brightness observation, and so the presence of RFI can bias geophysical parameter retrieval. As microwave radiometers typically have the most sensitive receivers operating in their band, low-level RFI is both significant and difficult to identify. The kurtosis statistic can be a powerful means of identifying some types of low-level RFI, as thermal noise has a distinct kurtosis value of three, whereas thermal noise contaminated even with low-level nonthermal RFI often has other values of kurtosis. This paper derives some benign distortions of the kurtosis statistic due to digitization effects and demonstrates these effects with a laboratory experiment in which a known amount of low-level RFI is injected into a digital microwave radiometer

The Sensitivity of Land Emissivity Estimates From AMSR-E as C and X Bands to Surface Properties

Microwave observations at low frequencies exhibit more sensitivity to surface and subsurface properties with little interference from the atmosphere. The objective of this study is to develop a global land emissivity product using passive microwave observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and to investigate its sensitivity to land surface properties. The developed product complements existing land emissivity products from SSM/I and AMSU by adding land emissivity estimates at two lower frequencies, 6.9 and 10.65 GHz (C- and X-band, respectively). Observations at these low frequencies penetrate deeper into the soil layer. Ancillary data used in the analysis, such as surface skin temperature and cloud mask, are obtained from International Satellite Cloud Climatology Project (ISCCP). Atmospheric properties are obtained from the TIROS Operational Vertical Sounder (TOVS) observations to determine the small upwelling and downwelling atmospheric emissions as well as the atmospheric transmission. A sensitivity test confirms the small effect of the atmosphere but shows that skin temperature accuracy can significantly affect emissivity estimates. Retrieved emissivities at C- and X-bands and their polarization differences exhibit similar patterns of variation with changes in land cover type, soil moisture, and vegetation density as seen at SSM/I-like frequencies (Ka and Ku bands). The emissivity maps from AMSR-E at these higher frequencies agree reasonably well with the existing SSM/I-based product. The inherent discrepancy introduced by the difference between SSM/I and AMSR-E frequencies, incidence angles, and calibration has been assessed. Significantly greater standard deviation of estimated emissivities compared to SSM/I land emissivity product was found over desert regions. Large differences between emissivity estimates from ascending and descending overpasses were found at lower frequencies due to the inconsistency between thermal IR skin temperatures and passive microwave brightness temperatures which can originate from below the surface. The mismatch between day and night AMSR-E emissivities is greater than ascending and descending differences of SSM/I emissivity. This is because of unique orbit time of AMSR-E (01:30 a.m./p.m. LT) while other microwave sensors have orbit time of 06:00 to 09:00 (a.m./p.m.). This highlights the importance of considering the penetration depth of the microwave signal and diurnal variability of the temperature in emissivity retrieval. The effect of these factors is greater for AMSR-E observations than SSM/I observations, as AMSR-E observations exhibit a greater difference between day and night measures. This issue must be addressed in future studies to improve the accuracy of the emissivity estimates especially at AMSR-E lower frequencies

Evaluating Radio Frequency Interference Detection Algorithms for SMAP (Soil Moisture Active Passive)

SMAP (Soil Moisture Active Passive) is a mission to be launched by NASA to measure soil moisture of the Earth’s land surface. The SMAP radiometer operates in the L-band protected spectrum (1400-1427 MHz) that is known to be vulnerable to radio frequency interference (RFI). Radiometric observations show substantial evidence of out-of-band emissions from neighboring transmitters and possibly illegally operating emitters. SMAP faces large levels of RFI and also significant amounts of low-level RFI equivalent to 0.1 K to 10 K of brightness temperature. Such low-level interference would be enough to jeopardize mission success without an aggressive mitigation solution. A decision has been made to employ an advanced digital microwave radiometer, the first of its kind for spaceflight, for use on SMAP. The mission takes a multi-domain approach to RFI mitigation utilizing an innovative on-board digital detector backend with DSP algorithms to detect and filter out harmful interference. Four different baseline RFI detectors are run on the ground and their outputs combined for a maximum probability of detection to remove RFI within a footprint. The SMAP radiometer outputs the first four raw moments of the receiver system noise voltage in 16 frequency channels for measurement of noise temperature and kurtosis as well as complex cross-correlation products for measuring the third and fourth Stokes parameters. Evaluating each of the four individual RFI detection algorithms is essential to ensure the highest efficiency produced by the maximum probability of detection. Receiver operating characteristic (ROC) curves are generated for each of the different detectors to evaluate performance. ROC curves plot the probability of detection versus false alarm rate. The optimum case would correspond to the highest probability of detection (PD) and lowest false alarm rate (FAR). A given threshold for the RFI algorithms would produce a corresponding (PD, FAR). The rest of the line curve is graphed by varying threshold from a minimal value to a maximal value. The ROC curves are performed on all different RFI algorithm detectors which include time-domain, cross-frequency, kurtosis, and polarization detectors. Each detector operates differently and behaves differently under different injected RFI. Different injected RFI include pulsed and sinusoidal at different frequencies, amplitudes, and power. The focus of the study is to optimize each of the given RFI detectors given any RFI signal. For example, since the cross-frequency algorithm uses only frequency resolution and no time resolution, its performance should be best for RFI that is localized in frequency. Since continuous wave (CW) RFI are localized in frequency by definition, as expected, the cross- frequency detector performed very well against CW RFI relative to other detectors. The RFI detection performance that is ultimately achieved will be a function of the threshold (that returns the highest PD versus lowest FAR), the nature of the RFI encountered, and radiometer system parameters such as the number of frequency channels and the integration period.NASA (Goddard Space Flight Center)SMAP MissionNo embargoAcademic Major: Electrical and Computer Engineerin