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 parametric study of aliasing error for a narrow field of view scanning radiometer

Starting from the general measurement equation, it is shown that a NFOV scanner can be approximated by a spatially invariant system whose point spread function depends on the detector shape and angular characteristics and electrical filter transfer function for given patches at the top of the atmosphere. The radiometer is modeled by a detector, electrical filter, analog to digital converter followed by a reconstruction filter. The errors introduced by aliasing and blurring into a reconstruction of the input radiant exitance are modeled and analyzed for various detector shapes, sampling intervals, electrical filters and scan types. Quantitative results on the errors introduced are presented showing the various tradeoffs between design parameters. The results indicate that proper selection of detector shape coupled with electrical filter can reduce aliasing errors significantly

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Frequency-modulated continuous-wave LiDAR compressive depth-mapping

We present an inexpensive architecture for converting a frequency-modulated continuous-wave LiDAR system into a compressive-sensing based depth-mapping camera. Instead of raster scanning to obtain depth-maps, compressive sensing is used to significantly reduce the number of measurements. Ideally, our approach requires two difference detectors. % but can operate with only one at the cost of doubling the number of measurments. Due to the large flux entering the detectors, the signal amplification from heterodyne detection, and the effects of background subtraction from compressive sensing, the system can obtain higher signal-to-noise ratios over detector-array based schemes while scanning a scene faster than is possible through raster-scanning. %Moreover, we show how a single total-variation minimization and two fast least-squares minimizations, instead of a single complex nonlinear minimization, can efficiently recover high-resolution depth-maps with minimal computational overhead. Moreover, by efficiently storing only $2m$ data points from $m<n$ measurements of an $n$ pixel scene, we can easily extract depths by solving only two linear equations with efficient convex-optimization methods

Centralized modeling of the communication space for spectral awareness in cognitive radio networks

The communication space is five dimensional: its degrees of freedom are frequency, time and space. The use of the electromagnetic spectrum depends on these parameters. With future applications such as opportunistic overlay access or distributed spectrum monitoring in mind, it is important to estimate the state of the communication space on the basis of incomplete or imprecise information. A promising approach are technology centric Cognitive Radio networks. In these networks, nodes cooperate to infer information on spectral occupancy. This conceptual paper proposes a novel approach for centralized modeling of the communication space with emphasis on spatial dependencies through the use of a regression model. The modeling approach is verified with practical measurements

Electromagnetic Wave Theory and Applications

Contains reports on twelve research projects.Joint Services Electronics Program (Contract DAALO3-86-K-0002)National Science Foundation (Grant ECS 85-04381)National Aeronautics and Space Administration/Goddard Space Flight Center (Contract NAG5-270)National Aeronautics and Space Administration/Goddard Space Flight Center (Contract NAG5-725)U.S. Navy - Office of Naval Research (Contract N00014-83-K-0258)U.S. Navy - Office of Naval Research (Contract N00014-86-K-0533)U.S. Army - Research Office Durham (Contract DAAG29-85-K-0079)International Business Machines, Inc.National Aeronautics and Space Administration/Goddard Space Flight Center (Contract NAG5-269)Simulation TechnologiesSchlumberger-Doll Researc