Damon Bradley

Radio-Frequency Interference Mitigation for the Soil Moisture Active Passive Microwave Radiometer

The Soil Moisture Active Passive (SMAP) radiometer operates in the L-band protected spectrum (1400-1427 MHz) that is known to be vulnerable to radio-frequency interference (RFI). Although transmissions are forbidden at these frequencies by international regulations, ground-based, airborne, and spaceborne radiometric observations show substantial evidence of out-of-band emissions from neighboring transmitters and possibly illegally operating emitters. The spectral environment that SMAP faces includes not only occasional large levels of RFI but also significant amounts of low-level RFI equivalent to a brightness temperature of 0.1-10 K at the radiometer output. This low-level interference would be enough to jeopardize the success of a mission without an aggressive mitigation solution, including special flight hardware and ground software with capabilities of RFI detection and removal. SMAP takes a multidomain approach to RFI mitigation by utilizing an innovative onboard digital detector back end with digital signal processing algorithms to characterize the time, frequency, polarization, and statistical properties of the received signals. Almost 1000 times more measurements than what is conventionally necessary are collected to enable the ground processing algorithm to detect and remove harmful interference. Multiple RFI detectors are run on the ground, and their outputs are combined for maximum likelihood of detection to remove the RFI within a footprint. The capabilities of the hardware and software systems are successfully demonstrated using test data collected with a SMAP radiometer engineering test unit.

Radio-frequency interference (RFI) mitigation for the soil moisture active/passive (SMAP) radiometer

The presence of anthropogenic RFI is expected to adversely impact soil moisture measurement by NASAs 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 [1] and Ruf [2].

On the Performance of Negentropy Approximations as Test Statistics for Detecting Sinusoidal RFI in Microwave Radiometers

Radio-frequency interference (RFI) is a persistent threat to Earth-observing microwave radiometers. A number of test statistics are used for radiometric RFI detection. This paper presents a new RFI detection method that uses the information theoretic quantity known as negentropy. In particular, we study six negentropy-based test statistics and compare their performance against kurtosis, Jarque-Bera, Anderson-Darling, and Shapiro-Wilk normality tests for specific RFI signal models. The Neyman-Pearson decision rule is used to develop receiver operating characteristic curves for each test statistic. We show that although negentropy can be used to detect RFI, it does not outperform kurtosis, except for the kurtosis blind-spot case.

Real-time beamforming synthetic aperture radar

This paper discusses the concept and design of a real-time Digital Beamforming Synthetic Aperture Radar (DBSAR) for airborne applications which can achieve fine spatial resolutions and wide swaths. The development of the DBSAR enhances important scientific measurements in Earth science, and serves as a prove-of-concept for planetary exploration missions. A unique aspect of DBSAR is that it achieves fine resolutions over large swaths by synthesizing multiple cross-track beams simultaneously using digital beamforming techniques. Each beam is processed using SAR algorithms to obtain the fine ground resolution without compromising fine range and azimuth resolutions. The processor uses an FPGA-based architecture to implement digital in-phase and quadrature (I/Q) demodulation, beamforming, and range and azimuth compression. The DBSAR concept will be implemented using the airborne L-Band Imaging Scatterometer (LIS) on board the NASA P3 aircraft. The system will achieve ground resolutions of less than 30 m and swaths of 10 km from an altitude of 8 km.

SMAP RFI mitigation algorithm performance characterization using airborne high-rate direct-sampled SMAPVEX 2012 data

The SMAP RFI detecting digital backend performance is characterized using real-environment L-band RFI data from the SMAPVEX 2012 campaign. Various types of RFI signals are extracted from the airborne campaign dataset and fed to the SMAP radiometer using an Arbitrary Waveform Generator (AWG). The backend detection performance is tested, and missed-detections are further investigated. Initial results indicate RFI detection performance for the SMAP digital backend is acceptable.

Detection performance of radar compressive sensing in noisy environments

In this paper, radar detection via compressive sensing is explored. Compressive sensing is a new theory of sampling which allows the reconstruction of a sparse signal by sampling at a much lower rate than the Nyquist rate. By using this technique in radar, the use of matched filter can be eliminated and high rate sampling can be replaced with low rate sampling. In this paper, compressive sensing is analyzed by applying varying factors such as noise and different measurement matrices. Different reconstruction algorithms are compared by generating ROC curves to determine their detection performance. We conduct simulations for a 64-length signal with 3 targets to determine the effectiveness of each algorithm in varying SNR. We also propose a simplified version of Orthogonal Matching Pursuit (OMP). Through numerous simulations, we find that a simplified version of Orthogonal Matching Pursuit (OMP), can give better results than the original OMP in noisy environments when sparsity is highly over estimated, but does not work as well for low noise environments.

The CubeSat Radiometer Radio Frequency Interference Technology Validation (CubeRRT) mission

The CubeSat Radiometer Radio Frequency Interference Technology Validation (CubeRRT) mission is developing a 6U CubeSat system to demonstrate radio frequency interference (RFI) detection and mitigation technologies for future microwave radiometer remote sensing missions. CubeRRT will perform observations of Earth brightness temperatures from 6-40 GHz using a 1 GHz bandwidth tuned channel, and will demonstrate on-board real-time RFI processing. The system is currently under development, with launch readiness expected in 2018 followed by a one year period of on-orbit operations. Project plans and status are reported in this paper.

Performance analysis of a hardware implemented complex signal kurtosis radio-frequency interference detector

In the field of microwave radiometry, Radio Frequency Interference (RFI) consistently degrades the value of scientific results. Through the use of digital receivers and signal processing, the effects of RFI on scientific measurements can be reduced depending on certain circumstances. As technology allows us to implement wider band digital receivers for radiometry, the problem of RFI mitigation changes. Our work focuses on finding a detector that outperforms real kurtosis in wide band scenarios. The algorithm implemented is a complex signal kurtosis detector which was modeled and simulated. The performance of both complex and real signal kurtosis is evaluated for continuous wave, pulsed continuous wave, and wide band quadrature phase shift keying (QPSK) modulations. The use of complex signal kurtosis increased the detectability of interference.

Wideband digital signal processing test-BED for radiometric RFI mitigation

RFI is a persistent and growing problem experienced by spaceborne microwave radiometers. Recent missions such as SMOS, SMAP, and GPM have all detected RFI in L, C, X, and K bands. To proactively deal with this issue, microwave radiometers must include digital back-end processors that generate data products that facilitate the detection and excision of RFI from desired brightness temperature measurements. The wideband digital signal processing testbed is a platform that allows rapid development of various RFI detection and mitigation algorithms using digital hardware akin to that which might be used for final spaceflight implementation. On it, we evaluate an improved version of the SMAP RFI Digital Signal Processor (DSP) that utilizes the new complex signal kurtosis algorithm as opposed to the real signal kurtosis that is used on the SMAP radiometer. In addition, we show how we scale the DSP to operate at 8.3 times the bandwidth of the SMAP radiometer for operation in K-band.

Ocean altimetry using wideband signals of opportunity

Coastal altimetry plays a prominent role in measuring the total water-level envelope directly, and is one of the key measurements required by storm surge applications and services. It can also provide important information about the wave field, leading to development of more realistic wave models and therefore improving forecasts of wave setup and overtopping processes. Satellite altimeters have a long history of mapping the variability of the Earths open ocean. However, this is not the case for coastal areas because of the limitations of technology and difficulties in processing and interpretation of data near coastal surface (due land contamination and rapid variations due to tides and atmospheric effects). There is, therefore, a need for more accurate Sea Surface Height (SSH) near coastal areas. Bistatic altimetry using signals of opportunity (SoOp) (e.g. digital communication signals) may provide additional measurements in coastal areas through oblique incidence angles and high bandwidth (400 MHz). In this study, we investigate the capabilities of SoOp technique for coastal altimetry from spaceborne platforms.