533 research outputs found

    GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype

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    The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector

    TriHex: combining formation flying, general circular orbits and alias-free imaging, for high resolution L-band aperture synthesis

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    The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), together with NASA’s Soil Moisture Active Passive (SMAP) mission, is providing a wealth of information to the user community for a wide range of applications. Although both missions are still operational, they have significantly exceeded their design life time. For this reason, ESA is looking at future mission concepts, which would adequately address the requirements of the passive L-band community beyond SMOS and SMAP. This article proposes one mission concept, TriHex, which has been found capable of achieving high spatial resolution, radiometric resolution, and accuracy, approaching the user needs. This is possible by the combination of aperture synthesis, formation flying, the use of general circular orbits, and alias-free imaging.Peer ReviewedPostprint (author's final draft

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    minimizing estimation error variance using a weighted sum of samples from the soil moisture active passive (SMAP) satellite

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    The National Aeronautics and Space Administration's (NASA) Soil Moisture Active Passive (SMAP) is the latest passive remote sensing satellite operating in the protected L-band spectrum from 1.400 to 1.427 GHz. SMAP provides global-scale soil moisture images with point-wise passive scanning of the earth's thermal radiations. SMAP takes multiple samples in frequency and time from each antenna footprint to increase the likelihood of capturing RFI-free samples. SMAP's current RFI detection and mitigation algorithm excludes samples detected to be RFI-contaminated and averages the remaining samples. But this approach can be less effective for harsh RFI environments, where RFI contamination is present in all or a large number of samples. In this paper, we investigate a bias-free weighted sum of samples estimator, where the weights can be computed based on the RFI's statistical properties

    BDS GNSS for Earth Observation

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    For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community

    Advancing the Monitoring Capabilities of Mountain Snowpack Fluctuations at Various Spatial and Temporal Scales

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    Snow is a critical water resource for the western US and many regions across the globe. However, our ability to accurately monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge in mountain regions. No single sensor currently has the ability to directly measure snow water equivalent (SWE) from space at a spatial scale suitable for water supply forecasting in mountain environments. This knowledge gap calls for the innovative use of remote sensing techniques, computational tools, and data science methods to advance our ability to estimate mountain snowpacks across a range of spatial and temporal scales. The goal of this dissertation is to advance our capabilities for understanding snowpack across watershed-relevant spatial and temporal scales. Two research approaches were used to accomplish this goal: quantifying the physiographic controls and sensitivities of hydrologically important snow metrics and progressing our ability to use L-band interferometric synthetic aperture radar (InSAR) to measure SWE changes. First, we quantify the physiographic controls and various snowpack metrics in the Sierra Nevada using a novel gridded SWE reanalysis dataset. Such work demonstrates the complexity of snowpack processes and the need for fine-resolution snowpack information. Next, using L-band Interferometric Synthetic Aperture Radar (InSAR) from the NASA SnowEx campaign, both snow ablation and accumulation are estimated in the Jemez Mountains, NM. The radar-derived retrievals are evaluated utilizing a combination of optical snow-cover data, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). Lastly, we compare multisensor optical-radar approaches for SWE retrievals and find that moderate-resolution legacy satellite products provide sufficient results. The results of this work show that L-band InSAR is a suitable technique for global SWE monitoring when used synergistically with optical SCA data and snowpack modeling. While two distinctive methods are present in this research, they both work towards advancing our ability to understand the dynamics of mountain snowpack

    1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

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    A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance
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