Analysis of RFI Identification and Mitigation in CAROLS Radiometer Data Using a Hardware Spectrum Analyser

A method to identify and mitigate radio frequency interference (RFI) in microwave radiometry based on the use of a spectrum analyzer has been developed. This method has been tested with CAROLS L-band airborne radiometer data that are strongly corrupted by RFI. RFI is a major limiting factor in passive microwave remote sensing interpretation. Although the 1.400–1.427 GHz bandwidth is protected, RFI sources close to these frequencies are still capable of corrupting radiometric measurements. In order to reduce the detrimental effects of RFI on brightness temperature measurements, a new spectrum analyzer has been added to the CAROLS radiometer system. A post processing algorithm is proposed, based on selective filters within the useful bandwidth divided into sub-bands. Two discriminant analyses based on the computation of kurtosis and Euclidian distances have been compared evaluated and validated in order to accurately separate the RF interference from natural signals

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

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

Soil Moisture Active Passive (SMAP) Project Algorithm Theoretical Basis Document SMAP L1B Radiometer Data Product: L1B_TB

The purpose of the Soil Moisture Active Passive (SMAP) radiometer calibration algorithm is to convert Level 0 (L0) radiometer digital counts data into calibrated estimates of brightness temperatures referenced to the Earth's surface within the main beam. The algorithm theory in most respects is similar to what has been developed and implemented for decades for other satellite radiometers; however, SMAP includes two key features heretofore absent from most satellite borne radiometers: radio frequency interference (RFI) detection and mitigation, and measurement of the third and fourth Stokes parameters using digital correlation. The purpose of this document is to describe the SMAP radiometer and forward model, explain the SMAP calibration algorithm, including approximations, errors, and biases, provide all necessary equations for implementing the calibration algorithm and detail the RFI detection and mitigation process. Section 2 provides a summary of algorithm objectives and driving requirements. Section 3 is a description of the instrument and Section 4 covers the forward models, upon which the algorithm is based. Section 5 gives the retrieval algorithm and theory. Section 6 describes the orbit simulator, which implements the forward model and is the key for deriving antenna pattern correction coefficients and testing the overall algorithm

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