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The 2-D frequency-based omega-K method is known to be a suitable algorithm for fully focused SAR (FF-SAR) radar altimeter processors, as its computational efficiency is much higher than equivalent time-based alternatives without much performance degradation. In this article, we provide a closed-form description of a 2-D frequency-domain omega-K algorithm specific for instruments such as Poseidon-4 onboard Sentinel-6. The processor is validated with real data from point targets and over the open ocean. Applications such as ocean swell retrieval and lead detection are demonstrated, showing the potentiality of the processor for future operational global-scale products., Mathematical Geodesy and Positioning

Since the 1960s, joint sensing and communication (JSAC) has been proposed as an attractive technique with advantages of enhanced spectral and hardware efficiency along with low latency. However, in those old days, complex transceiver designs hindered the massive adoption of JSAC in many applications. Nevertheless, thanks to advancing wireless technologies in recent years, JSAC has recently attracted substantial attention for a wide range of civil and military applications. In particular, JSAC enables simultaneous sensing and communication functionalities with the full cooperation of both operations in shared resources such as hardware as well as radio resources (i.e., frequency, time, space and so on). Note that sensing functionality is associated with other sub-functionalities of radio-detection-and-ranging (Radar), computation, and localization which are sporadically used terms in the literature. Thus, to generalize the concept and avoid any confusion, we use the term 'sensing' in the acronym JSAC as a consistent and general term associated with the other aforementioned sub-functionalities. This paper elaborates on defining sensing and communication operations and then, provides an overview with preliminaries, key latest findings, and state-of-the-art of JSAC. It also explores both existing and emerging Internet of Things (IoT) applications of JSAC. Next, it provides a new classification of JSAC technologies by taking into account not only the existing JSAC technologies but also a diverse range of technologies that allow JSAC to be used for various types of IoT applications. Eventually, this study projects future research directions and challenges of enabling JSAC in IoT.

To develop a 3-D imaging synthetic aperture radar(SAR) algorithm, a theoretical expression for a Fourier transform of data cube is required. In this article, we introduce a function representing the Fourier transform of 3-D SAR data cube. The function is derived using the method of stationary phase and similar to the one used for 2-D data matrix. For verification and evaluation, a 3-D range migration algorithm using this function for bulk compression is examined. The simulation results based on the parameters of an experimental terahertz indoor SAR testbed show that the algorithm can well focus the 3-D image and, hence, verify the derived function. The image quality assessments such as spatial resolutions and peak sidelobe ratio are used to further evaluate the function and the algorithm. The measured results are compared with the ones provided by a backprojection algorithm., open access

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (SM) across spatial scales. In this study, the single channel algorithm (SCA) is first applied to Soil Moisture and Ocean Salinity (SMOS) brightness temperatures at vertical polarization ( TBV ) to estimate SM at coarse resolution (25 km) and develop a land cover-specific and incidence angle (32.5°, 42.5°, and 52.5°)-adaptive calibration of single scattering albedo ( ¿ ) and soil roughness ( hs ) parameters. These effective parameters are used together with fine-scale multiangular Sentinel-1 backscatter in a single-pass active–passive downscaling approach to estimate TBV at fine scale (1 km) for each SMOS incidence angle. These TBVs are finally inverted to obtain the corresponding high-resolution SM maps. Results over the Iberian Peninsula for year 2018 show an increasing trend of ¿ and a decreasing trend of hs with SMOS incidence angle, with almost no variability of ¿ across land cover types. The active–passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fine TBV maps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolution SM maps have maximum differences in mean and standard deviation of 0.016 and 0.015 m 3 /m 3 , respectively, and compare well with in situ measurements. Our results indicate that model-based microwave approaches to estimate SM can be adequately adapted to account for the incidence angle diversity of planned missions, such as Copernicus Microwave Imaging Radiometer (CIMR), Radar Observing System for Europe in L-band (ROSE-L), and Sentinel-1 next generation., This work was supported in part by Ministeriode Ciencia e Innovación (MCIN)/Agencia Estatalde Investigación (AEI)/10.13039/501100011033 under Project PID2020-114623RB-C32; in part by the Spanish Ministry of Science, Innovation and Universities, through the coordinated project L-Band [MCIU/AEI/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE)]: Sobre la continuidad de las misiones satelitales de banda L. Nuevos paradigmas en productos y aplicaciones, under Grant ESP2017-89463-C3-2-R (UPC part) and Grant ESP2017- 89463-C3-1-R (ICM part); and in part by the Unidad de Excelencia María de Maeztu under Grant MDM-2016-0600. The work of María Piles was supported by project LEAVES under Grant RTI2018-096765-A-100 funded by MCIN/AEI/10.13039/501100011033 and by the European Regional Development Fund (ERDF, EU), Peer Reviewed, Postprint (author's final draft)

Spaceborne synthetic aperture radar (SAR) has been used for years to estimate high-resolution surface wind field from the ocean surface backscattered signal. Current SAR platforms have one single fixed antenna, and traditional inversion/retrieval schemes rely on one copolarized channel, leading to an unconstrained optimization problem for providing independent estimates of wind speed and direction. For routine application, this is generally solved with a priori information from the numerical weather prediction (NWP) model, inducing severe limitations for rapidly evolving meteorological systems where discrepancies can be significant between model and measurements. In this study, we investigate the benefit of having two simultaneous acquisitions with phase-preserving information in copolarization and cross polarization provided by Sentinel-1 (S-1). A comprehensive analysis of the co-cross-polarization coherence (CCPC) is performed to adequately estimate and calibrate CCPC values from S-1 interferometric wide (IW) mode images acquired over the ocean. A new polarimetric calibration (PolCAL) methodology based on least-squares (LS) criterion and direct matrix inversion is proposed yielding crosstalk estimates. We document CCPC odd symmetry with respect to relative wind direction for light to medium wind speeds (up to 14 m/s) and incidence angle from 30x00B0; to 45x00B0;. The azimuthal modulation is found to increase with both wind speed and incidence angle. An analytical model C-band polarimetric geophysical model function (CPGMF) is provided. The synergy of the CCPC with other radar parameters, such as backscattering coefficients or Doppler, to further constrain the inversion scheme is assessed, opening new perspectives for SAR-based wind field retrieval independent of any NWP model information.

This work presents an overview of the activity developed in the frame of a project funded by the European Space Agency (ESA). The research was focused on the study of the potential applications of GNSS Reflectometry (GNSS- R) over land, with an emphasis on soil moisture (SM) and biomass. A study about the sensitivity with respect to the freeze–thaw dynamics was considered as well. The work started with an analysis of the sensitivity of GNSS-R reflec- tivity collected by the TechDemoSat-1 (TDS-1) experimental satellite, although, to a limited extent, the Cyclone GNSS (CyGNSS) constellation was considered as well. The en - couraging sensitivity outcomes led to the development of retrieval algorithms: three different approaches for SM and one for biomass based on neural networks. A more theoretical investigation was carried out to better understand and predict the signal from a satellite platform which required the updating of two different models. To - pography effects and sensitivity to moisture and roughness of a rough soil were included as well as the effect of vegeta- tion cover. The project was carried out by a large team involving different research groups in Europe. It has led to main con- clusions and recommendations derived from a beneficial collaboration and fertilization of ideas. The primary ap - proaches and outcomes are summarized here, including comparisons to the recent literature., This study has been conducted in the framework of the ESA contract 4000120299/17/NL/AF/hh, “Potential of Spaceborne GNSS-R for Land Applications.” The TDS-1 data have been freely collected from the MERRBYS portal at merrbys.co.uk. The CyGNSS and SMAP data were downloaded from the NASA Earthdata portal at podaac.jpl.nasa.gov and nsidc.org/data/smap, respectively. The SMOS data are made available from the ESA through the smos-diss.eo.esa.int website. This work has also been partially supported by the project of the Ministerio de Ciencia, Innovación y Universidades and EU ERDF ref. RTI2018-099008-B-C21/AEI/10.13089/E01100011083, “Sensing with Pioneering Opportunistic Techniques,” and grant MDM-2016-0600 to the CommSensLab-UPC “María de Maeztu” Excellent Research Unit. We thank the ISMN for the availability of soil moisture data., Peer Reviewed, Postprint (author's final draft)

Oil spills in the Arctic are becoming more likely as shipping traffic increases in response to climate-related sea ice loss. To improve oil spill detection capability, we used a controlled mesocosm to analyze the multipolarized C-band backscatter response of oil in newly formed sea ice (NI). Artificial sea ice was grown in two cylindrical tubs at the Sea-ice Environmental Research Facility, University of Manitoba. The sea ice physical characteristics, including surface roughness, thickness, temperature, and salinity, were measured before and after oil injection below the ice sheet. Time-series C-band radar backscatter measurements detected the differences in the sea ice evolution and oil migration to the sea ice surface in the oil-contaminated tub, which was compared to uncontaminated ice in a control tub. Immediately prior to the presence of oil on the ice surface, the copolarized backscatter is increased by 13-dB local maximum, while the cross-polarized backscatter is decreased by 9-dB. Ice physical properties suggest that the local backscatter maximum and minimum, which occurred immediately before oil migrated onto the surface, were related to a combination of brine and oil upward migration. The findings of this work provide a baseline data interpretation for oil detection in the Arctic Ocean using current and future C-band multipolarization radar satellites., Funding agencies:Research Manitoba Canada Research Chair (CRC) Programs Natural Sciences and Engineering Research Council of Canada (NSERC) Canada Foundation for Innovation University of Manitoba GETS Program GENICE

This study focuses on the considerable spatial variability of precipitation along the western coast of a continent at mid-high latitude and investigates the precipitation climatology and mechanism along the south coast of Alaska, using datasets of spaceborne radars onboard two satellites, namely, the Dual-frequency Precipitation Radar (DPR) KuPR onboard the Global Precipitation Measurement (GPM) core satellite and the Cloud Profiling Radar (CPR) onboard CloudSat. At higher latitudes, differentiating the phase of precipitation particles falling on the ground is crucial in evaluating precipitation. Classification of satellite precipitation products according to the distance from the coastline shows that precipitation characteristics differ greatly on opposite sides of the coastline. Above coastal waters, relatively heavy precipitation with CPR reflectivity larger than 7 dBZ from orographically enhanced nimbostratus clouds, which can be detected by KuPR, is frequently captured. Meanwhile, along coastal mountains, light-to-moderate snowfall events with CPR reflectivity lower than 11 dBZ, which are well detected by the CPR but rarely detected by KuPR, frequently occur, and they are mainly brought by nimbostratus clouds advected from the coast and orographically enhanced shallow cumuliform clouds. There is no clear diurnal variation of precipitation except in summer, and the amplitude of the variation during summer is still low compared with total precipitation especially over the ocean, suggesting that the transport of synoptic-scale water vapor brings much precipitation throughout the year. Case studies and seasonal analysis indicate that frontal systems and moisture flows associated with extratropical cyclones that arrive from the Gulf of Alaska are blocked by terrain and stagnate along the coast to yield long-lasting precipitation along the coastline. The results of this study illustrate the importance of using complementary information provided by these radars to eva

This article describes the first results obtained from the Surface Waves Investigation and Monitoring (SWIM) instrument carried by the China France Oceanography Satellite (CFOSAT), which was launched on October 29, 2018. SWIM is a Ku-band radar with a near-nadir scanning beam geometry. It was designed to measure the spectral properties of surface ocean waves. First, the good behavior of the instrument is illustrated. It is then shown that the nadir products (significant wave height, normalized radar cross section, and wind speed) exhibit an accuracy similar to standard altimeter missions, thanks to a new retracking algorithm, which compensates a lower sampling rate compared to standard altimetry missions. The off-nadir beam observations are analyzed in detail. The normalized radar cross section varies with incidence and wind speed as expected from previous studies presented in the literature. We illustrate that, in order to retrieve the wave spectra from the radar backscattering fluctuations, it is crucial to apply a speckle correction derived from the observations. Directional spectra of ocean waves and their mean parameters are then compared to wave model data at the global scale and to in situ data from a selection of case studies. The good efficiency of SWIM to provide the spectral properties of ocean waves in the wavelength range [70500 m] is illustrated. The main limitations are discussed, and the perspectives to improve the data quality are presented.

Air- and spaceborne synthetic aperture radar (SAR) can provide a large number of high-resolution images for microwave remote sensing applications, such as geoscience and climate change research, environmental Earth system monitoring, and precision agriculture. Unlike spaceborne SAR, the flight trajectory of airborne SAR may deviate from its ideal straight course due to the influence of atmospheric turbulence, resulting in significant motion errors in radar raw data. If a standard imaging algorithm is directly used to process the data, the image focusing quality will be significantly degraded. Therefore, to improve image focusing quality, one typically needs to integrate standard imaging algorithms with an effective motion compensation (MoCo)/autofocus technique. In this article, we present a comprehensive overview of MoCo/autofocus methods for airborne SAR to articulate related research and promote further development.

In Winter 2017, the University of Massachusetts Amherst's Imaging Wind and Rain Airborne Profiler (IWRAP) was flown on a National Oceanic and Atmospheric Administration (NOAA) WP-3D Hurricane Hunter aircraft under the direction of scientists from Center for Satellite Applications and Research (STAR) at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) over the North Atlantic ocean out of Shannon, Ireland. IWRAP is a dual-frequency, conically scanning, profiling Doppler radar initially developed by Microwave Remote Sensing Laboratory (MIRSL) at the University of Massachusetts Amherst that is routinely installed on the NOAA WP-3D research aircraft. The flight on February 6, 2017, targeted a region of high winds (greater than 30 m/s) that was also observed by the Sentinel-1B satellite's synthetic aperture radar. Sentinel-1B was configured to observe in extended wide swath mode in both VV- and VH-polarizations, whereas the IWRAP C-band radar was configured to measure all of VV-, VH-, and HH-polarizations. IWRAP and Sentinel-1B VV and VH normalized radar cross section (NRCS) at the same Earthincidence angle along the flight path match reasonably well during the entire flight, but some additional trends between aircraft and satellite can be observed. IWRAP VV-polarized NRCS generally match the CMOD5.h geophysical model function (GMF), suggesting errors in the Sentinel-1B processing chain.

Under extreme weather conditions, the imprints of kilometer-scale marine atmospheric boundary layer roll vortices on the ocean surface are clearly visible in synthetic aperture radar (SAR) images of storms. Therefore, information about wind direction in storms can be obtained by analyzing SAR image features caused by boundary layer rolls. VH-polarized SAR imagery captures the structural features of storms well and shows prominent image gradients along the radial directions of the storm. The signal-to-noise ratios of VH-polarized images are small in low wind speed areas, but they are large in the same regions of VV-polarized images. Also, the capability of retrieving the atmospheric rolls orientation in VV-polarization is found to be sensitive to incidence angle, with better performances for larger incidence angles. Thus, there is the potential to retrieve the storm's wind directions using a combination of the VH- and VV-polarized SAR observations. In this article, we use the local gradient method to estimate tropical cyclone (TC) wind directions from C-band RADARSAT-2 and Sentinel-1A dual-polarization (VV & x002B; VH) SAR imagery. As a case study, wind directions with a spatial resolution of 25 km are derived by using both wide-swath VV- and VH-polarized SAR imagery over two hurricanes (Earl and Bertha) and one Typhoon (Meranti). We compare wind directions derived from ten dual-polarization SAR images with collocated wind directions from buoys, Global Positioning System (GPS) dropsondes, scatterometer, and radiometer. Statistical comparisons show that the wind direction bias and root-mean-square error are, respectively, -0.54 & x00B0; and 14.78 & x00B0; for VV-polarization, 0.38 & x00B0; and 14.25 & x00B0; for VH-polarization, 0.20 & x00B0; and 13.30 & x00B0; for VV- and VH-polarization, suggesting dual-polarization SAR is more suitable for the estimation of TC wind directions than VV- or VH-polarization SAR.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, To address the growing demand for accurate high-resolution ocean wind forcing from the ocean modeling community, we develop a new forcing product, ERA*, by means of a geolocated scatterometer-based correction applied to the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis or ERA-interim (hereafter referred to as ERAi). This method successfully corrects for local wind vector biases present in the ERAi output globally. Several configurations of the ERA* are tested using complementary scatterometer data [advanced scatterometer (ASCAT)-A/B and oceansat-2 scatterometer (OSCAT)] accumulated over different temporal windows, verified against independent scatterometer data [HY-2A scatterometer (HSCAT)], and evaluated through spectral analysis to assess the geophysical consistency of the new stress equivalent wind fields (U10S). Due to the high quality of the scatterometer U10S, ERA* contains some of the physical processes missing or misrepresented in ERAi. Although the method is highly dependent on sampling, it shows potential, notably in the tropics. Short temporal windows are preferred, to avoid oversmoothing of the U10S fields. Thus, corrections based on increased scatterometer sampling (use of multiple scatterometers) are required to capture the detailed forcing errors. When verified against HSCAT, the ERA* configurations based on multiple scatterometers reduce the vector root-mean-square difference about 10% with respect to that of ERAi. ERA* also shows a significant increase in small-scale true wind variability, observed in the U10S spectral slopes. In particular, the ERA* spectral slopes consistently lay between those of HSCAT and ERAi, but closer to HSCAT, suggesting that ERA* effectively adds spatial scales of about 50 km, substantially smaller than those resolved by global numerical weather prediction (NWP) output over the open ocean (about 150 km)., Peer Reviewed, Postprint (author's final draft)

Observations from spaceborne radar contain considerable information about vegetation dynamics.The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data.The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter from vegetated (agricultural) landscapes and to identify opportunities and challenges in this domain.Much of our understanding of vegetation backscatter from agricultural canopies stems from SAR studies to perform field-scale classification and monitoring.Hence, SAR applications, theory, and applications are considered here too.An overview will be provided of the knowledge generated from ground-based and airborne experimental campaigns that contributed to the development of crop classification, crop monitoring, and soil moisture monitoring applications.A description of the current vegetation modeling approaches will be given.A review of current applications of spaceborne radar will be used to illustrate the current state of the art in terms of data utilization.Finally, emerging applications, opportunities and challenges will be identified and discussed.Improved representation of vegetation phenology and water dynamics will be identified as essential to improve soil moisture retrievals, crop monitoring, and for the development of emerging drought/water stress applications., Water Resources

High-resolution inputs of rainfall are important in hydrological sciences, especially for urban hydrology. This is mainly because heavy rainfall-induced events such as flash floods can have a tremendous impact on society given their destructive nature and the short time scales in which they develop. With the development of technologies such as radars, satellites and (commercial) microwave links (CMLs), the spatiotemporal resolutions at which rainfall can be retrieved are becoming higher and higher. For the land surface of The Netherlands, we evaluate here four rainfall products, i.e., link-derived rainfall maps, Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) Final Run (IMERG--Global Precipitation Measurement mission), Meteosat Second Generation Cloud Physical Properties (CPP), and Nighttime Infrared Precipitation Estimation (NIPE). All rainfall products are compared against gauge-adjusted radar data, considered as the ground truth given its high quality, resolution, and availability. The evaluation is done for seven months at 30 min and 24,h. Overall, we found that link-derived rainfall maps outperform the satellite products and that IMERG outperforms CPP and NIPE. We also explore the potential of a CML network to validate satellite rainfall products. Usually, satellite derived products are validated against radar or rain gauge networks. If data from CMLs would be available, this would be highly relevant for ground validation in areas with scarce rainfall observations, since link-derived rainfall is truly independent of satellite-derived rainfall. The large worldwide coverage of CMLs potentially offers a more extensive platform for the ground validation of satellite estimates over the land surface of the Earth.

Autonomous vehicles rely on GPS aided by motion sensors to localize globally within the road network. However, not all driving surfaces have satellite visibility. Therefore, it is important to augment these systems with localization based on environmental sensing such as cameras, lidar and radar in order to increase reliability and robustness. In this work we look at using radar for localization. Radar sensors are available in compact format devices well suited to automotive applications. Past work on localization using radar in automotive applications has been based on careful sensor modeling and Sequential Monte Carlo, (Particle) filtering. In this work we investigate the use of the Iterative Closest Point, ICP, algorithm together with an Extended Kalman filter, EKF, for localizing a vehicle equipped with automotive grade radars. Experiments using data acquired on public roads shows that this computationally simpler approach yields sufficiently accurate results on par with more complex methods., QC 20160929, iQMatic VINNOVA

Global colocalized ocean surface measurements using the Global Precipitation Measurement near-nadir dual-frequency Ku- and Ka-band microwave measurements are analyzed and compared. Focusing on the Ka and Ku cross-sections fall-off with incidence angles, the contemporaneous measurements enable to more precisely document differing ocean scattering characteristics for both microwave frequencies. Sensitivity with wind speed and significant wave height is further reported using global comparisons with numerical estimates. As demonstrated, the bifrequency capability can provide direct means to efficiently separate short-scale wave contributions, between mean squared slope and curvature characteristics, and to further gain valuable insights concerning near-nadir instruments onboard future ocean satellite missions including the China-France Oceanography Satellite and the Surface Water Ocean Topography Mission.

Global Navigation Satellite Systems Reflectometry (GNSSR) is a sort of multi-static radar using navigation signals as signals of opportunity. Due to the large number of transmitting satellites multiple scattering points over the Earth's surface can be simultaneously tracked, which provides wide-swath and improved spatio-temporal sampling over current space-borne radar altimetry missions. The lack of experimental datasets from space covering signals from multiple constellations (GPS, GLONASS, Galileo and Beidou) at dual-band (L1 and L2), and dual-polarization (Right and Left Hand Circular Polarization: RHCP and LHCP), over the ocean, land, and cryosphere remains a bottleneck to further develop these techniques. 3Cat-2 is a 6 unit (3 × 2 elementary blocks of 10 × 10 × 10 cm3) cubesat mission designed and implemented at the Universitat Politecnica de Catalunya-BarcelonaTech. It will be launched in late July 2016. During one of the two stratospheric balloon flights of the 3Cat-2 payload, the P(Y) and C/A ReflectOmeter (PYCARO), it was found that the received signal power was nearly constant with the balloon height. This revealed the presence of a strong coherent component. This work presents the derivation of a unified equation to account for both incoherent and coherent scattering contributions., Peer Reviewed, Postprint (published version)

The Global Precipitation Measurement (GPM) Core Observatory will carry a Dual-frequency Precipitation Radar (DPR) consisting of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). In this study, 'at-launch' codes of DPR precipitation algorithms, which will be used in GPM ground systems at launch, were evaluated using synthetic data based upon the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. Results from the codes (Version 4.20131010) of the KuPR-only, KaPR-only, and DPR algorithms were compared with 'true values' calculated based upon drop size distributions assumed in the synthetic data and standard results from the TRMM algorithms at an altitude of 2 km over the ocean. The results indicate that the total precipitation amounts during April 2011 from the KuPR and DPR algorithms are similar to the true values, whereas the estimates from the KaPR data are underestimated. Moreover, the DPR estimates yielded smaller precipitation rates for rates less than about 10 mm/h and greater precipitation rates above 10 mm/h. Underestimation of the KaPR estimates was analyzed in terms of measured radar reflectivity ({\bf Z}-{\bf m}) of the KaPR at an altitude of 2 km. The underestimation of the KaPR data was most pronounced during strong precipitation events of {\bf Z}-{\bf m} \lt {\bf 18}~{\bf dBZ} (high attenuation cases) over heavy precipitation areas in the Tropics, whereas the underestimation was less pronounced when the {\bf Z}-{\bf m}\gt 26~{\bf dBZ} (moderate attenuation cases). The results suggest that the underestimation is caused by a problem in the attenuation correction method, which was verified by the improved codes., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9), pp.3931-3944; 2014

The TechSat21 space-based radar concept, suggested by AFRUVS, employs a cluster of free-floating satellites, each of which transmits its own orthogonal signal and receives all reflected signals. The satellites operate coherently at X-band. The cluster forms essentially a multi-element interferometer with a concomitant large number of grating lobes and significant ground clutter. This study explores some basic characteristics of such a system with respect to detection of slowly-moving ground targets (GMTI)., The original document contains color images.

At short wavelengths, especially C-, X-, and K-band, weather radar signals arc attenuated by the precipitation along their paths. This constitutes a major source of error for radar rainfall estimation, in particular for intense precipitation. A recently developed stochastic simulator of range profiles of raindrop size distributions (DSD) provides a controlled experiment framework to investigate the accuracy and robustness of attenuation correction algorithms. The work presented here focuses on the quantification of the influence of uncertainties concerning radar calibration, the parameterization of power law relations between the integral variables (radar reflectivity Z and specific attenuation k), and total path integrated attenuation (PIA) estimates at X-band. The analysis concerns single frequency, incoherent and non-polarimetric radar systems. Two attenuation correction algorithms, based on a forward and a backward implementation respectively, are studied. From DSD range profiles, the corresponding profiles of integral radar variables are derived. Using a Monte Carlo approach, the accuracy and robustness of the two algorithms are quantified for the different sources of error previously mentioned. This framework of realistic DSD variability provides a robust way to confirm that, under realistic assumptions concerning the PIA estimation uncertainty, the forward algorithm outperforms the backward algorithm for PIA values below 10 dB.

Spaceborne Synthetic Aperture Radar (SAR) techniques constitute an extremely promising alternative compared to traditional surveillance methods thanks to the all-weather and day-and-night capabilities of Radar linked with the large coverage of SAR images. Nowadays, the capabilities of satellite based SAR systems are confirmed by a wide amount of applications and experiments all over the world. Nevertheless, specific data exploitation methods are still to be developed to provide an efficient automatic interpretation of SAR data. The aim of this paper is to present an approach based on multiscale time–frequency analysis for the automatic detection of spots in a noisy background which is a critical matter in a number of SAR applications. The technique has been applied to automatic ship detection in single and multidimensional SAR imagery and it has proven to be a rapid, robust and reliable tool, able to manage complicated heterogeneous scenes where classical approaches may fail., Peer Reviewed

This volume contains the unclassified papers presented at the Sensors and Electronics Technology Panel symposium held on the Island of Samos, Greece on 16, 17 and 18 October 2000. All aspects of space-based observation including the satellite, support systems, sensors, data handling etc. Topics covered included: Operational requirements/trade-offs, Spaceborne radar technology, On-board/real-time processing, Specific space systems issues,Special features of optronics systems., Pres. at the RTO Sensors and Electronics Tech Panel (SET) Symposium, Island of Samos Greece, 16-18 Ocr 2000. For individual articles, see ADP010816 thru ADP010842

This paper presents a study of the interferometric coherence aimed to the understanding of the main sources of phase error of an operative wide band system. This objective is accomplished by finding a theoretical expression of coherence based on a suitable model capable of including the roughness of the surface, which produces decorrelation besides the conventional spectral overlapping approach. The analysis is validated by means of numerical simulations and controlled experiments in anechoic chamber, with the aim of extrapolating the obtained results to airborne and spaceborne imaging radars., Peer Reviewed