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The vital sign measurement systems inside vehicles have taken great or impulse in recent years, and the technologies that have been most welcomed are based on wireless measurements that do not interrupt driving and are imperceptible to the driver. There are disorders in breathing patterns, which include the movement of different regions of the human thorax mainly. In this sense, the objective of this paper is to determine the dimensions of a lens-based antenna system and the pointing angles of the radiation main lobe of the six antenna-elements to measure the elongation of six regions of the human torso and determine breathing patterns. The pointing angle is controlled by the position of the antenna behind the lens, and the beamwidth of the lobe depends on the directivity achieved by the antenna. An electronic system to simulate torso elongation is also presented to calibrate and test the sensitivity of the system. This work was supported by the Spanish “Comisión Interministerial de Ciencia y Tecnologia” (CICYT) under projects PID2019-107885GB-C31 and MDM2016-O6OO; Catalan Research Group 2017 SGR 219; and ”Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación” (SENESCYT) from the Ecuadorian government.

This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system., Comment: 8 pages, 11 figures, 5 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2021-08-10, 資料番号: PA2220049000

Electromagnetic (EM) waves propagate through the atmosphere where they are refracted depending on the composition of the atmosphere. This refraction highly influences the propagation of the EM-waves. Certain atmospheric conditions can cause EM-waves to get trapped within a duct. In these ducts blind zones may occur, where there is no EM-wave coverage. These blind zones show up in the results of ray tracing simulations. However, these codes provide no insight into the dependence of the blind zone on the atmospheric conditions and the transmitter height. In this research analytical expressions are derived for the range of these blind zones. The expressions have been verified by ray trace simulations for several transmitter heights under ducting conditions. Results show that the blind zone range can be accurately predicted.

The noncontact measurement of vital signs using ultra-wideband radar has been attracting increasing attention because it can unobtrusively provide information about the physical and mental condition of people. In particular, the continuous measurement of a person's time-varying instantaneous heart rate can estimate the activity level of the autonomic nervous system without the person wearing any sensors. Continuous heart rate measurement using radar is, however, a difficult task because accuracy is compromised by numerous factors, such as the posture and motion of the target person. In this study, we introduce techniques for increasing the accuracy and reliability of the noncontact measurement of heart rate variability. We demonstrate the performance of the proposed techniques by applying them to radar measurement data from a sleeping person, and we also compare its accuracy with electrocardiogram data.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2020-05-16, 資料番号: PA2110047000

In this paper, we present a novel unified framework to protect multi-function wireless systems from jamming attacks. Examples of such multi-function system include joint radar and communication (JRC) systems and simultaneous wireless information and power transfer (SWIPT) systems. By abstracting the system functionalities as a joint optimization problem of multiple queues, we achieve effective resistance against jammers for the multi-functions simultaneously.We incorporate different antijamming techniques into one framework. Deception mechanism is adopted to lure the jammer to attack and make its actions more predictable, and ambient backscatter technology is used to leverage the jamming signals. Since conventional Markov decision process (MDP) has only one decision epoch at every time slot, it cannot be used to model the deception strategy which needs two decision epochs to leverage the jamming signals. We therefore formulate the problem using an advanced two-step MDP. After that, a deep reinforcement learning algorithm with a prioritized double deep Q-Learning architecture is proposed to learn optimal strategies in different system states. We show that by jointly considering the multi-functions of the system with potential jamming attacks during design phase, significant improvement can be achieved for both of the system functionalities. Ministry of Education (MOE) National Research Foundation (NRF) This work was supported in part by Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), in part by the National Research Foundation, Singapore under the AI Singapore Programme (AISG) (AISG2-RP-2020-019), WASP/NTU Grant M4082187 (4080) and Singapore Ministry of Education (MOE) Tier 1 (RG16/20), in part by the Singapore International Graduate Award (SINGA) scholarship, the Australian Research Council (Discovery Early Career Researcher) under Award DE210100651, in part by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) under Grant 2021R1A2C2007638 and in part by ARC under Grants DP190101988 and DP210103410.

The detection of small ships in polarimetric synthetic aperture radar (PolSAR) images is still a topic for further investigation. Recently, patch detection techniques, such as superpixel-level detection, have stimulated wide interest because they can use the information contained in similarities among neighboring pixels. In this article, we propose a novel neighborhood polarimetric covariance matrix (NPCM) to detect the small ships in PolSAR images, leading to a significant improvement in the separability between ship targets and sea clutter. The NPCM utilizes the spatial correlation between neighborhood pixels and maps the representation for a given pixel into a high-dimensional covariance matrix by embedding spatial and polarization information. Using the NPCM formalism, we apply a standard whitening filter, similar to the polarimetric whitening filter (PWF). We show how the inclusion of neighborhood information improves the performance compared with the traditional polarimetric covariance matrix. However, this is at the expense of a higher computation cost. The theory is validated via the simulated and measured data under different sea states and using different radar platforms.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2019-04-15, 資料番号: PA2010014000

Target decomposition methods from polarimetric Synthetic Aperture Radar (PolSAR) data provides target scattering information. In this regard, several conventional model-based methods use scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. These techniques assume ad hoc scattering models within a radar resolution cell. Therefore, the use of several models makes the computation of scattering powers ambiguous. Some common issues of model-based decompositions are related to the compensation of the orientation angle about the radar line of sight and the occurrence of negative power components. We propose a model-free four-component scattering power decomposition that alleviates these issues. In the proposed approach, we use the nonconventional 3-D Barakat degree of polarization to obtain the polarization state of scattered electromagnetic wave. The degree of polarization is used to obtain the even-bounce, odd-bounce, and diffused scattering power components. Along with this, a measure of target scattering asymmetry is also proposed, which is then suitably utilized to obtain the helicity power. All the power components are roll-invariant, nonnegative, and unambiguous. In addition to this, we propose an unsupervised clustering technique that preserves the dominance of the scattering power components for different targets. This clustering technique assists in understanding the importance of diverse scattering mechanisms based on target characteristics. The technique adequately captures the clusters’ variations from one target to another according to their physical and geometrical properties. In this study, we utilized L-, C-, and X-band full-polarimetric SAR data. We used these three datasets to show the effectiveness of decomposition powers and the natural interpretability of clustering results. The code is available at: https://github.com/Subho07/MF4CF

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2021-02-16, 資料番号: PA2110068000

The attenuation of microwave emissions through the canopy is quantified by the vegetation optical depth (VOD), which is related to the amount of water, the biomass and the structure of vegetation. To provide microwave-derived plant water estimates, one must account for biomass/structure contributions in order to extract the water component from the VOD. This study uses Aquarius scatterometer data to build an L-band global seasonality of vegetation volume fraction (d), representative of biomass/structure dynamics. The dynamic range of d is adapted for its application in a gravimetric moisture (Mg) retrieval model. Results show that d ranging from 0 to 3.35.10- 4 is needed for modelling physically reasonable Mg values. The global average of d shows consistent spatial patterns across vegetation distributions, and d seasonality is coherent with the phenology of the studied vegetation types. These findings enable the separation of information on vegetation water and biomass/structure inherent within VOD. This work was supported by “la Caixa” Foundation (ID 100010434), under agreement LCF /PR/MIT19/51840001 (MIT -Spain Seed Fund), and by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (ERDF, EU) through projects ESP2017-89463-C3-3-R, RTI2018-096765-A-100, CAS19/00264 and MDM-2016-0600. Also, the authors are grateful to MIT for supporting this research with the MIT-Germany Seed Fund (D. Entekhabi, T. Jagdhuber).

This article presents a method for determining near-Pole ionization depletion regions and troughs from global navigation satellite system (GNSS) vertical total electron content (VTEC) maps. To define the regions, we use Image processing tools, considering the VTEC maps as gray-level images. In this work, watershed segmentation is applied to determine the edges of regions, based on the flow of areas between the lowest ionization zones (10%). In order to enhance the contrast between low-ionization regions and those with a higher relative ionization, the histogram of the maps is equalized, which increases the contrast of the VTEC maps, which is the distance between low-ionization regions and those with a higher relative ionization are increased. The use of percentile thresholds makes this method independent of hypotheses about the generation of depletions, their morphology, or the number of depletion regions that can occur on a map. We test the performance by comparing the depletion regions identified in eight events reported in the literature under different conditions of the ionosphere. The events used as reference were measured by satellites, radar, and GNSS. The method that we propose allows for real-time processing of GNSS VTEC maps. The morphology of the detected regions matches the shape that is reported in the literature. This work was supported in part by Ministerio de Ciencia e Inovación (MICINN) under Project PID2019-107579RB-I00, and in part by the European Commission (EC)-Funded Activity PolarGIMs2.

In this paper, we propose a method for continuous monitoring of vital signs-in particular, respiration frequency-with a commercial mm-wave radar. The nearly constant frequency (NCF) model is adopted to represent chest displacement due to respiration and simulate radar response. Based on this model, an extended Kalman filter (EKF) based estimator is developed to track the breathing frequency of a person. The impact of dynamic model parameters is investigated in numerical simulation. The possibility to track breathing frequency with the proposed method is demonstrated by experimental data processing.

In the framework of a feasibility study in support of the Hydroterra mission, a high resolution radar has been setup to monitor continuously a crop field with high resolution synthetic aperture radar images. In parallel, ground-truth data is acquired such as soil roughness, soil moisture and crop biological parameters. From this experimental data set the possibility to monitor soil moisture and crop biological parameters from back-scattering radar measurements will be assessed. To use the phase information of SAR images it is interesting to compensate the atmospheric phase screen induced by tropospheric refractive index. The paper presents a study of the atmosphetric phase drift prediction and compensation based on the local measurement of meteorological parameters.

Consistent sea ice monitoring requires accurate estimates of sea ice concentration. Current retrieval algorithms are based on low-resolution microwave radiometry data with limited penetration depth and are unable to resolve surface characteristics of sea ice in sufficient detail which is necessary to discriminate intact sea ice from closed water. Important information about surface roughness conditions are contained in the distribution of radar backscattering images which can be - in principle - used to detect melt ponds and different sea ice types. In this work, a two-step probabilistic approach based on Expectation-Maximization and Bayesian inference considers the spatial and statistical characteristics of medium-resolution daily-available Sentinel-1 SAR images. The presented method segments sea ice into a number of separable classes and enables to discriminate surface water from the remaining sea ice types. The lead author was supported by “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/D118/11660050, and received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673. The project was also funded through the award “Unidad de Excelencia María de Maeztu” MDM-2016-0600, by the Spanish Ministry of Science and Innovation through the project “L-band” ESP2017-89463-C3-2-R, and the project “Sensing with Pioneering Opportunistic Techniques (SPOT)” RTI2018-099008-B-C21/AEI/10.13039/501100011033.

Attenuation from clouds and precipitation hinders the use of Ka-band in SARs, radar altimeters and in satellite link communications. The NASA-JAXA Global Precipitation Measurement (GPM) mission, with its core satellite payload including a dual-frequency (13.6 and 35.5 GHz) radar and a multifrequency passive microwave radiometer, offers an unprecedented opportunity for better quantifying such attenuation effects. Based on four years of GPM products, this article presents a global climatology of Ka-band attenuation caused by clouds and precipitation and analyses the impact of the precipitation diurnal cycle. As expected, regions of high attenuation mirror precipitation patterns. Clouds and precipitation cause two-way attenuation at 35.5 GHz in excess of 3 dB about 1.5% of the time in the regions below 65$°, peaking at as much as 10% in the tropical rain belt and the South Pacific Convergence Zone and at circa 5% along the storm tracks of the North Atlantic and Pacific Oceans. Confirming previous findings, the diurnal cycle is particularly strong over the land and during the summer period; while over the ocean, the diurnal cycle is generally weaker some coherent features emerge in the tropical oceans and in the northern hemisphere. Results are useful for estimating data loss from (sun-synchronous) satellite adopting active instruments/links at a frequency close to 35 GHz.

International audience; Various correlation-based receivers have been proposed in passive bistatic and active monostatic radar exploiting information-bearing orthogonal frequency-division multiplexing (OFDM) transmissions, but too little has been dedicated to establishing their relations and advantages over each other. Accordingly, this paper provides an analytical comparison of the most commonly encountered filters, along with a performance analysis regarding three criteria: computational complexity, signal-to-interference-plus-noise-ratio and resilience to ground clutter. The last two especially assess the possible detrimental effects of the random sidelobes (or pedestal) induced by the data symbols in the range-Doppler map. Although simulations show that none of the filters performs unanimously better, the ones employing circular correlations globally evidence attractive results.

This article demonstrates for the first time, both numerically and experimentally, the feasibility of radar-based microwave imaging of anthropomorphic heterogeneously dense breasts in prone position, requiring no immersion liquid. The dry, contactless approach greatly simplifies the setup, favors patient comfort, and further avoids lengthy sanitation procedures after each exam. We use a radar-type technique with the antennas distributed in cylindrical configuration around the breast phantom. The reflectivity map is reconstructed using a wave-migration algorithm in the frequency domain. This article presents new developed strategies to cope with the challenges of a dry setup, namely increased skin artifact due to the concomitant absence of matching liquid and nonuniform breast shape. We propose an iterative and adaptive algorithm based on singular value decomposition that effectively removes the skin backscattering under the abovementioned conditions. It is compatible with automatic processing, and computationally fast. One of its inputs is the breast three-dimensional surface information, and its distance to the antennas, all obtained automatically from a proposed low-cost procedure based on a webcam. The imaging method is reasonably resilient to the presence of fibroglandular tissues, and to uncertainties of tissue permittivity. Another tackled challenge is the miniaturization of the antenna in air, which is achieved with an optimized balanced antipodal Vivaldi of the same size as counterparts used in dense immersion liquids. Finally, all the building blocks are combined to demonstrate experimentally the overall dry system performance, with very good detection of the tumor at three different positions in the breast, even in low-contrast scenarios. info:eu-repo/semantics/acceptedVersion

The Internet of Things (IoT) is made up of interconnected devices for exchanging information through sensors and actuators. One of the main physical sensors to understand the environment beyond the visible world is a radar. Basically, radars have always been a military tool to investigate the environment. However, with the developing technology, radars have become more compact and affordable to use in a building, in a car, in a drone, or even in a wristwatch. In the near future, radar-equipped IoT platforms will start to appear increasingly. For each IoT platform, dual use of spectrum with dual aperture is required for sensing and communicating when using conventional approaches. Emissions from the radar and communication circuitries are the main causes of the increase in energy consumption for any radar sensing IoT device. Furthermore, an increasing number of radars cause congested spectrum, and RF convergence between radar and communication systems becomes more likely to present itself. In recent years, numerous research works have proposed using the single waveform for perceiving the environment and sending information. They are often called "joint radar-communication' (JRC) systems. As a result of the latest advancements in JRC system designs, radar sensing IoT platforms now can be transformed into an "Internet of Radars" (IoR). This article is an attempt to introduce a prospective research direction in order to develop the architectures necessary to make the IoR concept possible. In this article, we present a short survey of JRC technologies likely to be used on radar-sensing- capable IoT devices. Then possible application areas, challenges to enable JRC, and future research perspectives are proposed., NA

In this paper, we devise adaptive decision schemes to detect targets competing against clutter and smart noise-like jammers (NLJ) which illuminate the radar system from the sidelobes. Specifically, the considered class of NLJs generates a pulse of noise (noise cover pulse) that is triggered by and concurrent with the received uncompressed pulse in order to mask the skin echo and, hence, to hide the true target range. The detection problem is formulated as a binary hypothesis test and two different models for the NLJ are considered. Then, ad hoc modifications of the generalized likelihood ratio test are exploited where the unknown parameters are estimated by means of cyclic optimization procedures. The performance analysis is carried out using simulated data and proves the effectiveness of the proposed approach for both situations where the NLJ is either active or switched off., 11 pages, 11 figures

The aim of radio-frequency (RF) sensing for assisted living is to deliver automatic support and monitoring for older people in their homes, impaired patients living independently, individuals in need of continuous support, and people suffering from chronic diseases that require them to stay in care-homes or at hospitals. RF sensing technologies have the potential to improve the quality of living of elderly people or disabled individuals in need of timely assistance. This paper provides a comprehensive review on three of the most innovative RF sensing technologies for activities of daily living in healthcare sector (namely active radar, passive radar, and wireless channel information and RFID sensing) and presents some of the open challenges that need to be addressed.

11 pages, 6 figures, 2 tables.-- © 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), The work was supported by the Spanish Research and Development Plan under the FPI grant (BES-2013-064521) from the project MIDAS-7 (reference AYA2012-39356-C05-03) and the R&D project L-BAND (reference ESP2017-89463-C3-2-R), With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2018-03-07, 資料番号: PA1810028000

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2017-03-07, 資料番号: PA1720013000

© 2019 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. Speckle noise and decorrelation can hamper the application and interpretation of PolSAR images. In this paper, a new adaptive multitemporal Pol(DIn)SAR filtering and phase optimization algorithm is proposed to address these limitations. This algorithm first categorizes and adaptively filters permanent scatterer (PS) and distributed scatterer (DS) pixels according to their polarimetric scattering mechanisms [i.e., the scattering-mechanism-based filtering (SMF)]. Then, two different polarimetric DInSAR (POLDInSAR) phase OPTimization methods are applied separately on the filtered PS and DS pixels (i.e., POLOPT). Finally, an inclusive pixel selection approach is used to identify high-quality pixels for ground deformation estimation. Thirty-one full-polarization Radarsat-2 SAR images over Barcelona (Spain) and 31 dual-polarization TerraSAR-X images over Murcia (Spain) have been used to evaluate the performance of the proposed algorithm. The PolSAR filtering results show that the speckle of PolSAR images has been well reduced with the preservation of details by the proposed SMF. The obtained ground deformation monitoring results have shown significant improvements, about ×7.2 (the full-polarization case) and ×3.8 (the dual-polarization case) with respect to the classical full-resolution single-pol amplitude dispersion method, on the valid pixels' densities. The excellent PolSAR filtering and ground deformation monitoring results achieved by the adaptive Pol(DIn)SAR filtering and phase optimization algorithm (i.e., the SMF-POLOPT) have validated the effectiveness of this proposed scheme.

Although typically associated with large-scale, defense -related use to monitor ships and aircraft, radar has been employed in the past few years for a number of short-range, civilian applications. We have discussed and presented some examples of radar used to support health-care provisions, to help monitor vital signs of patients at risk and their daily activities, a useful proxy for their more general physical and cognitive well-being. Unlike cameras and wearables, radar does not collect sensitive images of the people monitored or require users to wear, carry, or interact with new devices that may be perceived as intrusive; it can, therefore, have significant advantages in terms of users' perception and compliance. We have shown a few experimental results of the radar signatures for different human activities as well as an example of radar data tracking the respiratory rate of a monitored subject. The collection and full understanding of these data will be key to developing innovative signal processing and machine-learning algorithms to automate monitoring and consequently timely and proactive diagnostics for future healthcare provision.

The problem considered is that of estimating unambiguously migrating targets observed with a wideband radar. We extend a previously described sparse Bayesian algorithm to the presence of diffuse clutter and off-grid targets. A hybrid-Gibbs sampler is formulated to jointly estimate the sparse target amplitude vector, the grid mismatch, and the (assumed) autoregressive noise. Results on synthetic and fully experimental data show that targets can be actually unambiguously estimated even if located in blind speeds.

The growing need of large scale sensor networks for tracking of Unmanned Aerial Systems (UAS) results in high demand of effective algorithms which will provide automatic data fusion and produce human readable results. This paper presents a track-to-track data fusion system, where data from two independent sources are used to track Unmanned Aerial Vehicles (UAVs). The first source of data is Cooperative Surveillance System (CSS) trackers for UAVs, and the second source is Independent Non-cooperative Surveillance (INCS) from a ground based staring radar. The paper provides details on the whole process including: data pre-processing, association, analysis, fusion and output processing. Metrics and their influence on tracking results are also explained. Results of track-to-track data fusion of real-life experimental flight tests are provided. © 2019 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.

In this paper, an UWB Tapered Slot Vivaldi Antenna (TSA) is presented. The proposed TSA is optimized in order to increase the bandwidth and ensure a good impedance matching at low and bandwidth frequencies while respecting the low profile. The overall size of simulated antenna is 45×40×0.8 mm 3 . This antenna is designed in order to be used in bio medical application and some other radar based microwave imaging. This work was supported by the Moroccan Ministry of Higher Education (MESRSFC) and the CNRST under grant number PPR2/2015/36.

This paper addresses the challenges of making safe and predictable collision avoidance decisions considering uncertainties related to maritime radar tracking. When a maritime radar is used for autonomous collision avoidance, strategies for handling uncertain obstacle tracks, false tracks, and track loss become necessary. Robust decisions are needed in order to achieve clear and predictable actions according to the international regulations for preventing collisions at sea (COLREGs). We present robustness considerations and results of using an Integrated Probabilistic Data Association (IPDA) tracking method with a collision avoidance method based on Model Predictive Control. The results are from full-scale experiments that cover challenging multiple dynamic obstacle scenarios, including realistic vessel interactions where some obstacles obey COLREGs, while others do not. © 2019 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.

In this paper, we focus on the coexistence between a MIMO radar and cellular base stations. We study the interfering channel estimation, where the radar is operated in the "search and track" mode, and the BS receives the interference from the radar. Unlike the conventional methods where the radar and the cellular systems fully cooperate with each other, in this work we consider that they are uncoordinated and the BS needs to acquire the interfering channel state information (ICSI) by exploiting the radar probing waveforms. For completeness, both the line-of-sight (LoS) and Non-LoS (NLoS) channels are considered in the coexistence scenario. By further assuming that the BS has limited a priori knowledge about the radar waveforms, we propose several hypothesis testing methods to identify the working mode of the radar, and then obtain the ICSI through a variety of channel estimation schemes. Based on the statistical theory, we analyze the theoretical performance of both the hypothesis testing and the channel estimation methods. Finally, simulation results verify the effectiveness of our theoretical analysis and demonstrate that the BS can effectively estimate the interfering channel even with limited information from the radar., 15 pages, 10 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Antenna effects alter the detection of buried objects during ground-penetrating radar (GPR) surveys. In this paper, we propose a novel approach based on full-wave inversion to filter out antenna effects from GPR data. The approach, which is exact for locally planar layered media, resorts to a recently developed electromagnetic model that takes advantage of an intrinsic, closed-form solution of Maxwell’s equations to describe the antenna–medium system. As any multilayered medium can be reduced to a half-space medium with effective, frequency-dependent, global reflection coefficients, the method consists in inverting the radar data to retrieve a frequency-dependent, half-space complex conductivity. Converted into the time domain, this quantity represents the filtered radar image. We validated the approach through numerical simulations and laboratory experiments with pipes buried in a sandbox. The results demonstrated the validity of the concept and showed that the filtered radar images include only medium reflections, which means an easier interpretation in terms of medium structures. Antenna radiation pattern effects are, however, not removed. This physically based approach including the full-wave antenna model appears to be very promising for improved subsurface imaging and provides the basis for multifrequency GPR data fusion as the source is inherently normalized.

This paper investigates a radar-communications waveform sharing scenario. Particularly, it addresses the self-interference phenomenon induced by independent single-point scatterers throughout a low-complexity monostatic orthogonal frequency-division multiplexing (OFDM)-based radar receiver from a statistical viewpoint. Accordingly, an analytical expression of the postprocessing signal-to-interference-plus-noise-ratio is derived and detection performance is quantified in simulated scenarios for rectangular and nonrectangular pulses. Both metrics suggest that this phenomenon must be further handled.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2016-06-20, 資料番号: PA1720008000

© 2018 IEEE. Imaging through fog has important applications in industries such as self-driving cars, augmented driving, airplanes, helicopters, drones and trains. Here we show that time profiles of light reflected from fog have a distribution (Gamma) that is different from light reflected from objects occluded by fog (Gaussian). This helps to distinguish between background photons reflected from the fog and signal photons reflected from the occluded object. Based on this observation, we recover reflectance and depth of a scene obstructed by dense, dynamic, and heterogeneous fog. For practical use cases, the imaging system is designed in optical reflection mode with minimal footprint and is based on LIDAR hardware. Specifically, we use a single photon avalanche diode (SPAD) camera that time-tags individual detected photons. A probabilistic computational framework is developed to estimate the fog properties from the measurement itself, without prior knowledge. Other solutions are based on radar that suffers from poor resolution (due to the long wavelength), or on time gating that suffers from low signal-to-noise ratio. The suggested technique is experimentally evaluated in a wide range of fog densities created in a fog chamber It demonstrates recovering objects 57cm away from the camera when the visibility is 37cm. In that case it recovers depth with a resolution of 5cm and scene reflectance with an improvement of 4dB in PSNR and 3.4x reconstruction quality in SSIM over time gating techniques.

Technological developments in low-power mmW radar allow the measurement of deformations in the order of few micrometers and monitoring dynamic processes like structural vibration. This paper presents a fast acquisition FMCW radar operating at 94 GHz and recent experimental results of remote observation of micrometric vibration motion for bridge structural health monitoring. The presented approach offers cost reduction and applicability advantages compared to conventional techniques.

Multilooking is an effective speckle reduction tool for InSAR images. However, it has some limitations. Based on the De-specKS and NL-SAR algorithms, an adaptive multilooking scheme for multi-temporal InSAR data has been proposed in this paper. Simulated and real SAR data sets have been employed to evaluate the performance of this scheme. The results show that, by taking advantages of DespecKS and NL-SAR, the proposed scheme presents better detail preservation and speckle noise reduction performances comparing with these two advanced and classical multilooking algorithms.

This paper presents a novel automatic real-time remote sensing algorithm that uses radar images and global positioning satellite system measurements to estimate the ice-drift velocity vector in a region around a free-floating and potentially moving vessel. It is motivated by the low image frequency of satellite systems together with the inconvenience of deploying and retrieving ice trackers (beacons) on the ice. The algorithm combines radar image processing with two Kalman filters to produce the estimated local drift vector decoupled from the ship motion. The proposed design is verified using a full-scale data set from an ice management operation north of Svalbard in 2015. It is found that the performance of the algorithm is comparable with that of trackers on the ice. © 2018 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.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission [1] has been in orbit for over 8 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions is working well. The data for this whole period has been and is being processed with the operational version of the current Level-l processor (version v620). Also a representative part of the same data set has been processed with a working version of a new processor (v720) which is now in preparation so that homogenous records of brightness temperatures have been made available. These rich and long data records have allowed learning important lessons from the in-flight experience, and shall eventually lead into the consolidation of the new Level-l processor version (v720) with its corresponding auxiliary calibration and configuration files. Once the improvements are confirmed the new processor version shall be recommended for the operational chain.

This is a postprint (author final draft) version of article that has been accepted for publication. A fully version can be found at: https://doi.org/10.1109/TGRS.2018.2851064 © 2018 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. Among the different techniques available for measuring the atmospheric water vapor content, Raman lidars stand out as accurate instruments providing detailed profiles with high temporal and altitude resolution. Their principle is based on obtaining the range-resolved ratio of the lidar signals corresponding to Raman returns from water vapor and nitrogen molecules, which is proportional to the water vapor mixing ratio. To do this, it is necessary to determine a calibration factor, specific of each lidar instrument. A method for obtaining this parameter, based on zenith measurements of diffuse sunlight, on Raman scattering models and on simulations, using a radiative transfer model, to estimate sky radiances at the wavelengths of interest, has been applied to the lidar system of Universitat Politècnica de Catalunya (UPC; Technical University of Catalonia, Barcelona, Spain). A set of calibrations, performed between 2016 and 2017, has permitted assessing the calibration procedure and analyzing the stability of the calibration factor in the UPC instrument. Results show that although the calibration factor can remain stable for long periods of time, it can suffer sudden variations that make indispensable to implement a convenient and reliable procedure to perform regular calibrations. We show that the method, which can be applied to any lidar with water vapor and nitrogen Raman channels, can completely dispense with radiosonde data. The calibration method is validated by comparison with simultaneous radiosonde water vapor measurements. Limitations of radiosondes for validating--and eventually calibrating--water vapor Raman lidars have been revealed.

Collision avoidance systems are a key ingredient in developing autonomous surface vehicles (ASVs). Such systems require real-time information about the environment, which can be obtained from transponder-based systems or exteroceptive sensors located on the ASV. In this paper, we present a closed-loop collision avoidance (COLAV) system using a maritime radar for detecting target ships, implemented on a 26 foot high-speed ASV. The system was validated in full-scale experiments in Trondheimsfjorden, Norway, in May 2017. The probabilistic data association filter (PDAF) is used for tracking target vessels. The output from the PDAF is processed through a least-squares retrodiction procedure in order to provide the COLAV system with sufficiently accurate course estimates. A tracking interface provides estimates of target states to the COLAV system, which is based on the dynamic window (DW) algorithm. DW is a reactive COLAV algorithm originally designed for ground vehicles, and we therefore make a number of modifications to adapt it for use with high-speed ASVs. The closed-loop experiments demonstrated successful COLAV with this system, but also disclosed several challenges arising from both the DW algorithm and the tracking system, motivating for further work. © 2018 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.

We discuss the challenge of managing the Multifunction Phased Array Radar (MPAR) timeline to satisfy the requirements of its multiple missions, with a particular focus on weather surveillance. This command and control (C2) function partitions the available scan time among these missions, exploits opportunities to service multiple missions simultaneously, and utilizes techniques for increasing scan rate where feasible. After reviewing the candidate MPAR architectures and relevant previous research, we describe a specific C2 framework that is consistent with a demonstrated active array architecture using overlapped subarrays to realize multiple, concurrent receive beams. Analysis of recently articulated requirements for near-airport and national-scale aircraft surveillance indicates that with this architecture, 40–60% of the MPAR scan timeline would be available for the high-fidelity weather observations currently provided by the Weather Service Radar (WSR-88D) network. We show that an appropriate use of subarray generated concurrent receive beams, in concert with previously documented, complementary techniques to increase the weather scan rate, could enable MPAR to perform full weather volume scans at a rate of 1 per minute. Published observing system simulation experiments, human-in-the-loop studies and radar-data assimilation experiments indicate that high-quality weather radar observations at this rate may significantly improve the lead time and reliability of severe weather warnings relative to current observation capabilities., United States. National Oceanic and Atmospheric Administration (Contract FA8721-05-C-0002), United States. National Oceanic and Atmospheric Administration (Contract FA8702-15-D-0001)

Remote sensing of soil moisture has reached a level of good maturity and accuracy for which the retrieved products are ready to use in real-world applications. Due to the importance of soil moisture in the partitioning of the water and energy fluxes between the land surface and the atmosphere, a wide range of applications can benefit from the availability of satellite soil moisture products. Specifically, the Advanced SCATterometer (ASCAT) on board the series of Meteorological Operational (Metop) satellites is providing a near real time (and long-term, 9+ years starting from January 2007) soil moisture product, with a nearly daily (sub-daily after the launch of Metop-B) revisit time and a spatial sampling of 12.5 and 25 km. This study first performs a review of the climatic, meteorological, and hydrological studies that use satellite soil moisture products for a better understanding of the water and energy cycle. Specifically, applications that consider satellite soil moisture product for improving their predictions are analyzed and discussed. Moreover, four real examples are shown in which ASCAT soil moisture observations have been successfully applied toward: 1) numerical weather prediction, 2) rainfall estimation, 3) flood forecasting, and 4) drought monitoring and prediction. Finally, the strengths and limitations of ASCAT soil moisture products and the way forward for fully exploiting these data in real-world applications are discussed.

One of the main applications of SAR interferometry is the monitoring of hazard risks related with urban subsidence and landslide instabilities. The different sensors available offer different carrier frequencies, image swaths and resolutions. Depending on the extension of the area and phenomena to monitor, one particular mode can be more suitable than others. In this paper the benefits of high-resolution X-band for landslide monitoring is addressed. The selected mode is the experimental Staring-Spotlight of TerraSAR/TanDEM-X and the area under study the landslide of El Forn de Canillo (Andorra) that is perfectly oriented for orbital DInSAR processing. The paper demonstrates the benefits of high resolution data and the processing challenges that have to be addressed.

Durability of concrete structures depends mainly on the ease whereby water and any aggressive chemical agents dissolved therein can penetrate. Therefore, measuring water penetrability in concrete structures is crucial mostly when structures are in service. In this context, nondestructive techniques play an important role. In particular, the electromagnetic waves emitted by ground-penetrating radar (GPR) are very sensitive to the water content of the medium through which they propagate. This fact provides an interesting opportunity to analyze if the GPR technique allows the assessment of water penetrability in concrete with enough accuracy. In line with this, this paper describes the laboratory experiments and relevant analysis carried out to study the capability of GPR to assess water penetrability in hardened concrete. For this purpose, concrete specimens were fabricated and dried in an oven after 90 days of curing. They were then dipped into water and GPR measurements were taken at different intervals, based on coupling a 2.0 GHz antenna. The results showed that the agreement between velocity increments and the waterfront advance was excellent. In addition, a specific processing of the data acquired was developed. This process included the isolation of the reflection due to the waterfront, produced just before the reflection of the bottom of the samples. As a result of this processing, the in-depth waterfront location at different times was determined with high reliability., This work was supported by the Universitat Politecnica de Valencia (Spain) under the PAID-06-12 research plan through a project entitled: "Analisis de la durabilidad del hormigon por medio de la tecnica no destructiva del georradar."

Trabajo presentado a la IEEE Conference on Roboticand Automation (ICRA), celebrada en Estocolmo (Suecia) del 16 al 21 de mayo de 2016., Localization is the key perceptual process closing the loop of autonomous navigation, allowing self-driving vehi- cles to operate in a deliberate way. To ensure robust localization, autonomous vehicles have to implement redundant estimation processes, ideally independent in terms of the underlying physics behind sensing principles. This paper presents a stereo radar odometry system, which can be used as such a redundant system, complementary to other odometry estimation processes, providing robustness for long-term operability. The presented work is novel with respect to previously published methods in that it contains: (i) a detailed formulation of the Doppler error and its associated uncertainty; (ii) an observability analysis that gives the minimal conditions to infer a 2D twist from radar readings; and (iii) a numerical analysis for optimal vehicle sensor placement. Experimental results are also detailed that validate the theoretical insights., This work was supported by the EU Project CargoAnts: Cargo Handling by Automated Next Generation Transportation Systems for Ports and Terminals (FP7-SST-2013-RTD-1-605598).

Global Navigation Satellite Systems-Reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multi-static radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from a ground-based and airborne experiments, but studies using space-borne data are still preliminary. This work presents a sensitivity study of Using TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e. vegetation covers). Despite the scattering in the data, which can be attributed to the temporal and spatial (footprint size) collocation mismatch with the SMOS and MODIS NDVI data, and errors in the land use data preliminary results show a good correlation with soil moisture.

This work presents an improvement of the P2EPS end-to-end simulator to assess the performances of Global Navigation Satellite System Reflectometry (GNSS-R) space missions over land. Beyond the single specular reflection model, topography effects are included. In order to avoid the computational burden of facet approach based on the bi-static radar model, the Phong reflection model is employed. By calculating the delay, Doppler frequency, and relative intensity of scattered points, the topography effects can be accounted for as a sort of impulse response in the delay, and Doppler domains to be convolved with the Woodward Ambiguity function.

The paper analyzes the problem of achieving a coherent operation in Geostationary SAR (GEOSAR) missions intended for continuous monitoring of land surfaces. In contrast to LEOSAR missions, GEOSAR uses very long SAR integration times (from minutes to hours). Accordingly phase errors due to orbit perturbations, radar master oscillator drift, atmosphere propagation and other sources must be conveniently compensated during SAR processing to avoid image defocusing. A network of Active Radar Calibrators (ARC) distributed on the observed scene is proposed, providing echo envelope and phase observations before Synthetic Aperture Processing. In this way the radar antenna phase center trajectory and other phase error sources can be continuously tracked and compensated using a pyramidal sub-aperture processing approach.