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

A review of RFI mitigation techniques in microwave radiometry

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.Peer ReviewedPostprint (published version

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

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

Radio frequency interference in microwave radiometry: statistical analysis and study of techniques for detection and mitigation

Microwave radiometry field has been increasing its performance with higher accuracy measurements, leading to a more presence in the remote sensing field. Several space-borne, air-borne and ground-based radiometers have been developed to perform measurement campaigns; however, the actual sensitivity of a radiometer is often limited by man-made radio emissions such as radars, broadcasting emissions, wireless communications and many other communication systems based on electromagnetic waves, limiting the improvement in the radiometers¿ performance. Consequently, in order to maintain the accuracy in the radiometric measurements, it has been researched in the Radio Frequency Interference (RFI) detection and mitigation systems and algorithms for the microwave radiometry field. The scope of this doctoral thesis is the development and testing of RFI detection and mitigation algorithms in order to enhance radiometric measurements performed by the Multifequency Experimental Radiometer with Interference Tracking for Experiments over Land and Littoral (MERITXELL). The MERITXELL has been developed during this thesis with the idea studying the RFI present in several radiometric bands and the way to mitigate it, as well as to obtain data from diverse frequency bands and devices in only one measurement campaign

Performance and requirements of GEO SAR systems in the presence of Radio Frequency Interferences

Geosynchronous Synthetic Aperture Radar (GEO SAR) is a possible next generation SAR system, which has the excellent performance of less than one-day revisit and hundreds of kilometres coverage. However, Radio Frequency Interference (RFI) is a serious problem, because the specified primary allocation frequencies are shared by the increasing number of microwave devices. More seriously, as the high orbit of GEO SAR makes the system have a very large imaging swath, the RFI signals all over the illuminated continent will interfere and deteriorate the GEO SAR signal. Aimed at the RFI impact in GEO SAR case, this paper focuses on the performance evaluation and the system design requirement of GEO SAR in the presence of RFI impact. Under the RFI impact, Signal-to-Interference-plus-Noise Ratio (SINR) and the required power are theoretically deduced both for the ground RFI and the bistatic scattering RFI cases. Based on the theoretical analysis, performance evaluations of the GEO SAR design examples in the presence of RFI are conducted. The results show that higher RFI intensity and lower working frequency will make the GEO SAR have a higher power requirement for compensating the RFI impact. Moreover, specular RFI bistatic scattering will give rise to the extremely serious impact on GEO SAR, which needs incredible power requirements for compensations. At last, real RFI signal behaviours and statistical analyses based on the SMOS satellite, Beidou-2 navigation satellite and Sentinel-1 A data have been given in the appendix

Contributions to radio frequency interference detection and mitigation in Earth observation

Radio Frequency Interference (RFI) is the most common problem for electronic measuring systems. The presence of those electromagnetic waves can harm the measurements taken from very sensitive instruments, like microwave radiometry or navigation systems. The accuracy and precision are compromised. A first step to mitigate those unwanted effects is to study the RFI properties. Different algorithms have been proposed to detect the interferences, but there is no method that works in all cases. The scope of this dissertation is the design, implementation and testing of different detection and mitigation methods in real-time. Performed surveys and characterization of RFI sources provide a great contribution to optimize the current mitigation techniques. In the mitigation area, two real-time hardware systems have been implemented: a wavelet denoise system to model the RFI and mitigate it, and a circuit to allow a navigation system to continue operational under the effects of a jammer.El problema més comú en els sistemes electrònics de mesura són les interferències electromagnètiques. La presència d'aquests senyals pot danyar les mesures preses per instruments molt sensibles, com radiòmetres de microones o sistemes de navegació. L'exactitud i precisió es veuen compromeses. El primer pas per mitigar aquests efectes no desitjats és estudiar les propietats de les interferències electromagnètiques. Diversos algoritmes han estat proposats per detectar interferències, però no hi ha mètode que funcioni bé en tots els casos . Aquest treball comprèn el disseny, implementació i comprovació de diferents mètodes de detecció i mitigació en temps real. Els estudis i caracterització de les fonts d'interferències són una gran contribució per a optimitzar les tècniques de mitigació actuals. En el tema de mitigació, dos sistemes en temps real han estat implementats en hardware: un sistema que utilitza wavelets per modelar la interferència i mitigar-la, i un circuit que permet a un sistema de navegació continuar funcionant sota els efectes d'un interferidor comercial ( jammer )

Development of Radio Frequency Interference Detection Algorithm for Passive Microwave Remote Sensing

Radio Frequency Interference (RFI) signals are man-made sources that are increasingly plaguing passive microwave remote sensing measurements. RFI is of insidious nature, with some signals low power enough to go undetected but large enough to impact science measurements and their results. With the launch of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite in November 2009 and the upcoming launches of the new NASA sea-surface salinity measuring Aquarius mission in June 2011 and soil-moisture measuring Soil Moisture Active Passive (SMAP) mission around 2015, active steps are being taken to detect and mitigate RFI at L-band. An RFI detection algorithm was designed for the Aquarius mission. The algorithm performance was analyzed using kurtosis based RFI ground-truth. The algorithm has been developed with several adjustable location dependant parameters to control the detection statistics (false-alarm rate and probability of detection). The kurtosis statistical detection algorithm has been compared with the Aquarius pulse detection method. The comparative study determines the feasibility of the kurtosis detector for the SMAP radiometer, as a primary RFI detection algorithm in terms of detectability and data bandwidth. The kurtosis algorithm has superior detection capabilities for low duty-cycle radar like pulses, which are more prevalent according to analysis of field campaign data. Most RFI algorithms developed have generally been optimized for performance with individual pulsed-sinusoidal RFI sources. A new RFI detection model is developed that takes into account multiple RFI sources within an antenna footprint. The performance of the kurtosis detection algorithm under such central-limit conditions is evaluated. The SMOS mission has a unique hardware system, and conventional RFI detection techniques cannot be applied. Instead, an RFI detection algorithm for SMOS is developed and applied in the angular domain. This algorithm compares brightness temperature values at various incidence angles for a particular grid location. This algorithm is compared and contrasted with other algorithms present in the visibility domain of SMOS, as well as the spatial domain. Initial results indicate that the SMOS RFI detection algorithm in the angular domain has a higher sensitivity and lower false-alarm rate than algorithms developed in the other two domains.Ph.D.Atmospheric and Space SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86308/1/samisra_1.pd