Search

Gesture Recognition which is an inevitable part of human computer interaction is an ever evolving active research area. This has been in use under varied applications like smart home systems, sign language recognition, augment reality and in device controls. The acquisition of hand gestures are usually through optical sensors, in this work a radar based hand gesture data set is used for classifying the gestures. Micro doppler signatures were used as the input to the model. Radar dataset has fewer data samples compared to optical based data sets. The proposed work uses separable convolutional neural networks model which does depth wise convolution followed by point wise convolution to reduce overfitting effect of the training data. The proposed model was built in such a way that the model is capable of classifying any unseen data without exactly mimicking the training samples. The proposed model has achieved 94.56% as the testing accuracy which is certainly better than the previous work on this Dop Net data set. Moreover the model has also minimized the computational hours of the model using separable convolutions.

Nyquist Folding Receiver (NYFR) is a perceptron structure that realizes a low probability of intercept (LPI) signal analog to information. Aiming at the problem of LPI radar signal receiving, the time domain, frequency domain, and time-frequency domain problems of signals intercepted by NYFR structure are studied. Combined with the time-frequency analysis (TFA) method, a radar recognition scheme based on deep learning (DL) is introduced, which can reliably classify common LPI radar signals. First, the structure of NYFR and its characteristics in the time domain, frequency domain, and time and frequency domain are analyzed. Then, the received signal is then converted into a time-frequency image (TFI). Finally, four kinds of DL algorithms are used to classify LPI radar signals. Simulation results demonstrate the correctness of the NYFR structure, and the effectiveness of the proposed recognition method is verified by comparison experiments.

Stealth aircraft use Stealth Technology in order to evade recognition by utilizing mix of highlights with the goal that Aircraft can invade with RADAR and furthermore decrease the deceivability in “radio frequency (RF) range”, sound and infrared visual. During the most recent decades, stealth technology has demonstrated to be one of the best methodologies to the extent the undertaking to escape radar systems is concerned. Stealth intends to evade the recognition or attempt to cover up, for planes, avoiding radar implied stealth. The idea utilized in the stealth innovation isn’t so difficult. It utilizes the guideline to retain and mirror the radar waves. Airplane redirects the radar waves in different ways and limits the radar waves which return back to radar. Another marvel which permits the air ship maintain completely the incoming radar wavelengths to inhabit the electromagnetic reincorporated energy in various way. The structure and material used to manufacture air ship choose the degree of stealth accomplish via air ship. The thought for the radar radio wire to release Radio energy that is then returned through any item that occurs to recognize. The time taken for the reflection to come is measure by the radar radio wire and it can tell how far off an object is.

A recent paper (Mhaskar (2020)) introduces a straightforward and simple kernel based approximation for manifold learning that does not require the knowledge of anything about the manifold, except for its dimension. In this paper, we examine how the pointwise error in approximation using least squares optimization based on similarly localized kernels depends upon the data characteristics and deteriorates as one goes away from the training data. The theory is presented with an abstract localized kernel, which can utilize any prior knowledge about the data being located on an unknown sub-manifold of a known manifold. We demonstrate the performance of our approach using a publicly available micro-Doppler data set, and investigate the use of different preprocessing measures, kernels, and manifold dimensions. Specifically, it is shown that the localized kernel introduced in the above mentioned paper when used with PCA components leads to a near-competitive performance to deep neural networks, and offers significant improvements in training speed and memory requirements. To demonstrate the fact that our methods are agnostic to the domain knowledge, we examine the classification problem in a simple video data set.

Ultra-thin microwave absorbers have been urgently demanded for electromagnetic applications in recent years. Herein, porous carbon with a "flower cluster" microstructure was synthesized from biomass waste (mango seeds) by a facile activation and carbonization method. The novel structure reduced the density and also improved the impedance matching, dipole polarization, and provided many carbon matrix-air interfaces for interfacial polarization, resulting in superior microwave absorption performance. At an ultra-thin thickness of 1.5 mm, extraordinary microwave absorption was achieved, with a reflection loss (RL) of -42 dB. The effective absorption bandwidth reached 4.2 GHz. The RL can be further improved to -68.4 dB by adjusting the amount of activator to manipulate the structure of porous carbon. In addition, from the simulated radar scattering results, the maximum reduction in the radar cross-section (RCS) reached 30.4 dBm

Excavation is common in the construction of hydropower projects. Monitoring for both stable and unstable slopes is a pressing requirement during progressive excavations with or without cut-slope support measures. The present study is about an excavation carried out in a proposed concrete gravity dam site on the right bank slope of the Punatsangchhu River in western Bhutan. During the excavation, the right bank abutment witnessed multiple events of slope failures of various magnitudes. One major landslide occurred on 23 July 2013 in the toe of the right abutment, where foliation/multiple sheared/fractured zones/seams in quartz-feldspathic biotite gneiss dip towards the valley. During further excavations/piling works, a subsequent landslide on the downstream, within the body of the July 2013 landslide, occurred on 12 August 2016. As a result, a real-time monitoring of the slope became necessary to facilitate further excavations for achieving the dam foundation. Thus, the advanced slope monitoring instrument like Image By Interferometric Survey – Frequency Modulated (IBIS-FM) radar was deployed for monitoring the right bank slope during the excavation for the construction of ∼129 m high dam. The displacement of the hill slope is assessed based on this system by monitoring point locations as well as areas by assigning different threshold values for providing timely alerts. This real-time monitoring was effective in identifying the reactivation of August 2016 landslide that occurred on 22 January 2019. Thus, this study showcases the efficiency of IBIS-FM radar in monitoring slope instability with sub-millimeter accuracy on a near real-time basis.

Airborne Distributed Coherent Aperture Radar (ADCAR) is one of the most promising next-generation radars to significantly improve target detection and discrimination abilities. However, time and phase synchronization among unit radars should be done before an ADCAR is intended to cohere on a potential target. To address this problem, a time and phase synchronization technique using clutter observations is proposed in this paper. Clutter returns from different azimuths and elevations on the surface of the earth are employed to calibrate system uncertainties. Two stages are mainly considered: a scene registration among range-Doppler units from different transmit/receive pairs is performed to enhance the clutter coherence in the first stage, followed by a joint estimation of those synchronization errors in the second stage. To relieve the computational burden, a novel Separable and Sequential Estimation (SSE) method is provided to separate the unknowns at the sacrifice of a range-Doppler unit. Moreover, performance analyses including the clutter coherence ability, estimation lower bound, and signal coherence loss are also performed. Finally, simulation results indicate that ADCAR time and phase synchronization is realized by using our methods.

In low signal-to-noise ratio (SNR) environments, the traditional radar emitter recognition (RER) method struggles to recognize multiple radar emitter signals in parallel. This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network. This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs. First, we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform (STFT). The time-frequency distribution image is then denoised using a deep normalized convolutional neural network (DNCNN). Secondly, the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established, and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model. Finally, time-frequency image is recognized and classified through the model, thus completing the automatic classification and recognition of the time-domain aliasing signal. Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.

Radar Beam-Park Experiments (BPEs) are regularly conducted in order to get insights into the statistical distribution of the space debris environment, especially at low altitudes. Signal processing techniques are necessary to process the data collected during BPEs. An improvement in these techniques may result in a corresponding improvement in terms of radar detection performance and space object parameter estimation. To this end, a transition from incoherent to coherent pulse integration could be of help, thanks to the resulting improvement in terms of Signal to Noise Ratio (SNR). Before assessing the potential of new signal processing schemes, the expected variations of the radar observables over the acquisition time have to be investigated. Indeed, deriving a priori bounds on the possible values the observables can assume, would allow adapting the BPE settings (e.g., the radar waveform or the boundaries of the matched filter) in order to optimize the processing speed and the overall detection performance. To carry out this investigation, two BPEs were simulated and the resulting beam crossing lists were exploited to understand how the satellite parameter space maps into the radar parameter subspace. This paper presents and discusses the results of the two simulations.

Globally the land subsidence is a significant problem of the rapidly growing urban area. The factor responsible for the land subsidence caused by over-exploitation of the underground fluid such as water, petroleum, and gas respectively. In present study we present the result of detail investigation of active ground subsidance in New Delhi, National Capital Region (NCR). This area indicates a high rate of urban growth during the past decades. To analyze the land subsidence, we used multiple SAR sensor data and exploited the PS-InSAR technique. The data used for this study are Cosmo-skymed acquired between 08/06/2011 to 15/11/2017, Sentinel-1A-B (18-12-2014 to 27-11-2018), and ALOS PALSAR acquired between 19/01/2007 and 20/01/2011. These radar sensors operate in X, C, and L-band, which covers over ten years, from 2007 to 2018. The PSI results of Cosmo-skymed reveals that the Delhi NCR region has undergone an average deformation ± 15 mm/y, a maximum surface deformation observed from ALOS-PALSAR is 10 to 18 mm/y and the ground displacement observed from the SENTINAL-1A data is −2 to 16 mm/y. Groundwater level data also collected for the same period and a ground water level depletion compared with the subsidence. Monitoring land subsidence with ground-based conventional technology is time-consuming and can be carried out in a limited area due to the financial implication. PS-InSAR is an established method to detect the surface movement using the SAR sensor's time-series data. The result shows that a twenty centimeter of land subsidence is visible in some areas, validated with the collected ground evidence. The affected area is also showing resemblance to the groundwater depleting condition in those areas. This study also establish that multiple sensor data can be used to monitor the long term land subsidence.

Traditional clustering algorithms can be applied for the pre-sorting step of radar signal sorting. It can effectively dilute the pulse stream and prevent the dense pulse stream from interfering pulse repetition interval (PRI) extraction. However, the pre-sorting deviation will cause interference and missing pulses during the main sorting process. To solve this problem, we deploy the unmanned aerial vehicle (UAV) swarm to monitor reconnaissance areas and put forward a novel deep transfer learning based signal sorting method. The UAV swarm can collect the pulses from different time and spatial domains, and interference and missing pulses in main sorting processing can be relieved dramatically. In our model, we pre-train our model with the data collected from multiple source areas, which corresponds to different areas detected by different parts of UAV swarms. Then we fine-tune our model with the data of the target area. The experimental results prove that the signal sorting accuracy of methods based on deep transfer learning, i.e., YOLO-MobileNet, F-RCNN and cascade RCNN, are higher than that of the baseline methods. In addition, the signal sorting accuracy of traditional methods based on deep learning can be greatly improved with the help of transfer learning.

Upcoming Synthetic Aperture Radar (SAR) missions with larger swath like NASA-ISRO Synthetic Aperture Radar (NISAR) (~250 km) requires a large, flat and homogeneous low-background site, free from human-made structures for point targets deployment for calibration. Finding out such a large, flat, homogeneous area devoid of perceived sources of radar clutter is a challenging task. In this regard, Antarctica is a potential site for calibration as it fulfills many of the criteria required for the ideal calibration site and suitable for setting up a corner reflector (CR) network for SAR calibration. This network will help in the calibration study and aid the studies related to Interferometric SAR (InSAR) viz. ice velocity estimation. As a part of this activity, in-house designed and developed CRs were installed near Indian research base stations Maitri and Bharati at Antarctica during the 38th Indian Scientific Expedition to Antarctica (ISEA) during 2018–2019. Corner reflectors were designed keeping in mind the harsh environment of Antarctica, such as high katabatic winds, blizzards, snowfall and low temperature. Temporal and seasonal analysis of radar backscatter data using available Indian Radar Imaging Satellite (RISAT-1) and European SENTINEL-1 SAR satellite over Maitri and Bharati was carried out to find out the suitable CR deployment site. Super-hydrophobic microwave transparent cover was designed and installed over CRs to protect snow accumulation. Hydrophobic radar absorbing materials were installed over central-mount to decrease the background noise due to it. Various tests such as structural analysis of total CR system, thermal analysis of materials and Radar Cross Section (RCS) characterization of CR were done before sending it to Antarctica. Integrated Wide Swath (IW) mode data of Sentinel-1 satellite was used for analyzing time-series response of CR in SAR image.

In current wireless communication and electronic systems, digital signals and electromagnetic (EM) radiation are processed by different modules. Here, we propose a mechanism to fuse the modulation of digital signals and the manipulation of EM radiation on a single programmable metasurface. The programmable metasurface consists of massive subwavelength-scale digital coding elements. A set of digital states of all elements forms simultaneous digital information roles for modulation and the wave-control sequence code of the programmable metasurface. By designing digital coding sequences in the spatial and temporal domains, the digital information and far-field patterns of the programmable metasurface can be programmed simultaneously and instantly in desired ways. For the experimental demonstration of the mechanism, we present a programmable wireless communication system. The same system can realize transmissions of digital information in single-channel modes with beam-steerable capability and multichannel modes with multiple independent information. The measured results show the excellent performance of the programmable system. This work provides excellent prospects for applications in fifth- or sixth-generation wireless communications and modern intelligent platforms for unmanned aircrafts and vehicles.

Cotton is an important cash crop in the world, as the main source of natural and renewable fiber for textiles. Accurate and timely monitoring of the cotton distribution is crucial for cotton cultivation management and international trade. However, most of the previous researches on cotton identification using remotely sensed images are highly dependent on training samples, and the collection of samples is time-consuming and expensive. To overcome this limitation, a new index, termed as Cotton Mapping Index (CMI), was developed in this study for automatic cotton mapping using time series of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) satellite data. Four sites in the United States (U.S.) and four sites in China were selected to develop and assess the performance of the CMI. The spectral characteristics derived from Sentinel-2 and backscattering coefficients derived from Sentinel-1 for cotton and non-cotton crops during the cotton growth period were analyzed. Considering the phenology differences of crops in different regions, the features at an adaptive window were adopted to construct the CMI. The results showed that at the peak greenness period, the multiplication of red-edge 1 and red-edge 2 band for cotton samples were much larger than those for non-cotton samples, whereas the spectral angle at the red band as well as the absolute values of backscattering coefficients in vertical transmit and vertical receive (VV) polarization for cotton samples were much smaller than those for non-cotton samples. Based on these findings, the CMI was developed to identify cotton cultivated area within the cropland area. The overall accuracy of classification results for the sites in the U.S. was higher than 81.20%, and the mean relative error for the sites in Xinjiang of China was 26.69%. The CMI, which incorporated optical and radar features, had a better performance than the indices using optical features solely. The advantage of the CMI over supervised classifiers (i.e., k-nearest neighbors, support vector machine and random forest) is that no training samples are required. Moreover, the cotton distribution map can be obtained before the harvest using the CMI. These results indicated the potential of the CMI for cotton mapping. The applicability of CMI in other regions with different cropping systems and crop types needs to be further assessed in the future study.

Toward an ultralight antenna structure for a preliminary radar satellite with a 30 m class phased array antenna as a midway target for future solar power satellites, this study examines the feasibility of an antenna structure made of an ultrathin plate mounted with multiple antenna patches and strips. In this structure, the thermal deformation caused by the difference in the coefficients of thermal expansion of each constituent is a critical issue to guarantee the flatness requirement from the aspect of electromagnetic performance. In this study, the thermal deformation of this structure was regarded as a generalized eigenstrain problem and was studied theoretically. In the theoretical model, Eshelby's equivalent inclusion method and Mori–Tanaka's mean-field method were combined in the framework of the classical lamination theory. The effects of the in-plane geometry, alignment, and layered structure of the antenna components on the deformation of the entire antenna structure were investigated to obtain the design guidelines. Based on these results, the optimized layered structure was examined for the suppression of thermal deformation.

Low-frequency radar tomography is an important subsurface imaging method for future planetary missions to solar system small bodies. We derive angular and radial sampling criteria for monostatic and bistatic radar tomography algorithms that are based on monochromatic free-space backprojection and spherical apertures. We use the vector Born approximation to highlight the degeneracy of monochromatic bistatic source/receiver direction pair measurements in k-space. Analytical expressions are then derived for the scalar point target response of different spherical sampling geometries. We also derive the angular sampling step and total number of sampling points required to fully reconstruct the point target response for monostatic, bistatic, and non-degenerate k-space spherical apertures. These are evaluated for object sizes and radar operating frequencies expected in small body tomography. We also analyze and derive expressions for the coherence loss of spherical apertures due to random errors in a sensor’s radial position, which provides requirements on the a posteriori ephemeris knowledge. Finally, we derive a vector backprojection algorithm suitable for focusing quad-pol scattering matrix (S-matrix) data that is tested using full-wave S-matrix simulations of dielectric point targets. This work is intended to aid radar instrument performance analysis and inform the design and architecture of future instruments and missions.

The presence of clouds is widely identified as the primary uncertainty in current surface solar global horizontal irradiance (GHI) forecasts. Despite a wealth of historical satellite-derived irradiance observations, only limited research has investigated this problem from a purely data-driven perspective, something that has seen tremendous success in related domains such as radar- and satellite-based precipitation short-term forecasting. This paper presents IrradianceNet, a novel satellite-based neural network for spatiotemporal forecasting of surface solar irradiance up to 4 h in the future over Europe. Our method is fully data-driven and needs no post-processing or calibration based on sparse ground-based measurements of irradiance. We demonstrate superior forecasting performance compared to several persistence models, the TV - L 1 algorithm, and ERA5 reanalysis data for satellite-derived solar irradiance using the European SARAH-2.1 dataset. We also validate these results using ground-based pyranometer observations from the Baseline Surface Radiation Network. Our conclusions remain unchanged when we account for hourly and monthly seasonality. Finally, applying a simple cloud mask scheme, we demonstrate that our performance improvement arises due to a considerable reduction in cloudy pixel errors. This is initial evidence that purely data-driven methods might better approximate and infer future cloud dynamics and their impact on surface solar irradiance.

As small satellites are finding increased applicability in a number of space missions, the community is actively exploring the feasibility of missions employing active instruments on small spacecraft. In particular, synthetic aperture radars are of interest as they prove to be versatile tools for Earth Observation purposes. This paper proposes a trade space exploration model to identify designs for space-borne satellite synthetic aperture radars that address the desired instrument requirements. Two trade space analysis approaches are proposed, based on a multivariate analysis with two evaluation metrics and using parallel coordinate plots. The paper demonstrates the utility of the trade space model by considering two case studies. The first study looks at radar instruments on a broad range of small satellite platforms. This study narrows down a trade space of 1265 feasible radar designs to less than 44 Pareto optimal designs at different center frequencies (C- and X-band), elicits conditions for L-band SARs feasibility, and discusses the feasibility bounds and technical constraints on associated instrument and spacecraft requirements. The second study considers radars for 3U CubeSat platforms. The study identifies 44 Pareto optimal designs within 12,928 feasible designs and sheds light on the operational constraints required for the development of such innovative miniaturized radars.

The Asteroid (469219) 2016 HO 3 , one of the targets of the potential small celestial body missions, is a typical small-sized stable quasi-satellite of the Earth. Compared with the targets of previous impact missions, it is more challenging for a hypervelocity impact on 2016 HO 3 due to its much smaller size. Besides, the time durations for autonomous navigation and maneuver correction are greatly narrowed because the impactor can hardly capture the small-sized target until they are close enough. This paper suggests a high-precision autonomous navigation system and proposes a minimum-impact-error correction strategy for the targeting maneuver to solve the challenges. The navigation system utilizes traditional optical navigation assisted by a radar ranging measurement unit, which has been proved of high precision and short convergence time during the approach phase (better than 10 m in 100.0 s). After obtaining the relative positions and velocities, the targeting maneuvers are needed to correct the impactor’s orbit for a final successful impact. Therefore, a minimum-impact-error correction strategy is proposed to determine the optimal number and correction schedule of targeting maneuvers, which can minimize the impact error under the condition that the total velocity increments are limited. In the case of the 2016 HO 3 impactor, this paper conducts numerical Monte-Carlo simulations for validation. The results are encouraging, and the proportion of successful impact is above 99.1 percent. The correction strategy proposed in this paper is meaningful for future hypervelocity impact on small celestial bodies.

A new method is proposed for improving the IRI2016 vertical electron density profile by ingesting the in situ Ne values from Swarm A, B and C. The method is validated by comparing the improved electron density profiles with measured full height profiles over three Incoherent Scatter Radar stations in the American sector (Jicamarca, Millstone Hill and Sondrestrom) during the period 5–11 September 2017 covering both quiet and storm days. We considered 90 epochs corresponding to times when one of the Swarm satellites passed near an Incoherent Scatter Radar at a time when the Incoherent Scatter Radar was measuring electron density profiles. The method results in improvements of the electron density profile, as measured in terms of the Root-Mean-Square-Error in 63% of the observed epochs. The fraction of improvements on quiet days is 63% of the observed epochs, while 37% of the improvements occurred on storm days. The change in Root-Mean-Square-Error of the IRI profile compared to the ISR profiles before and after the modification was not dependent on the distance between the observation location and the ISR, nor on the difference between the hmF2 predicted by the IRI and measured by the ISR.

The free and open data policy of Sentinel-1 SAR images enables Radar interferometry (InSAR) to perform time series surface deformation monitoring over large areas. InSAR deformation monitoring and prediction can investigate the freeze-thaw cycles of permafrost on the Qinghai-Tibet Plateau. However, the convolutional and recurrent neural networks cannot accurately model long-term and complex relations in multivariate time series data, it is challenging to implement time series deformation prediction with high spatial resolution. In this paper, an innovative InSAR deformation prediction integrated algorithm based on the transformer models is proposed to predict time series deformation more accurately surrounding Salt Lake. Compared with the other solutions, the unique feature of the proposed method is that: 1) this method takes advantage of the self-attention mechanism to study complicated dynamic deformation features of permafrost caused by temperature and other variables from InSAR time series deformation. 2) The transformer-based model can more accurately simulate seasonal and non-seasonal deformation signals, and is effective for short-term prediction of surface deformation in permafrost areas. The InSAR deformation prediction results demonstrate that the InSAR deformation prediction method achieves better prediction performance in predicting the deformation trends of permafrost with a point scale compared with the prediction results of other models. Based on the predicted deformation and the water extraction results, the expansion trends surrounding Salt Lake are discussed and evaluated. The total area of Salt Lake increased by 57.32 km

Lightning has strong destructive powers; its blast wave, high temperature, and high voltage can pose a great threat to human production, life, and personal safety. The destructive power of high-intensity lightning is much greater than that of low-intensity lightning. The estimation of lightning intensity can provide an important reference for determining the lightning protection level and lightning disaster risk assessment. Lightning is a type of small-scale severe convective weather phenomenon. Weather radar is one of the best monitoring systems that can frequently sample the detailed three-dimensional (3D) structures of convective storms, with a small spatial scale and short lifetime at high temporal and spatial resolutions. Therefore, it is possible to extract the 3D spatial feature strongly correlated with lightning from 3D weather radar for estimating lightning intensity. This paper proposes a Vision Transformer model for lightning intensity estimation that can automatically estimate lightning intensity from 3D weather radar data. In an experiment, we transferred the task of estimating lightning intensity into a multicategory classification task. A framework was designed to produce lightning feature samples for model input from 3D weather radar and lightning location data. Then, the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm was used to balance and optimize the sample distribution. Finally, samples were input into the proposed lightning intensity estimation model based on Vision Transformer for training and evaluation. Experimental results show that the proposed model based on Vision Transformers performs well with lightning intensity estimation.

Remote sensing techniques are of particular interest for monitoring wildfire effects on soil properties, which may be highly context-dependent in large and heterogeneous burned landscapes. Despite the physical sense of synthetic aperture radar (SAR) backscatter data for characterizing soil spatial variability in burned areas, this approach remains completely unexplored. This study aimed to evaluate the performance of SAR backscatter data in C-band (Sentinel-1) and L-band (ALOS-2) for monitoring fire effects on soil organic carbon and nutrients (total nitrogen and available phosphorous) at short term in a heterogeneous Mediterranean landscape mosaic made of shrublands and forests that was affected by a large wildfire. The ability of SAR backscatter coefficients and several band transformations of both sensors for retrieving soil properties measured in the field in immediate post-fire situation (one month after fire) was tested through a model averaging approach. The temporal transferability of SAR-based models from one month to one year after wildfire was also evaluated, which allowed to assess short-term changes in soil properties at large scale as a function of pre-fire plant community type. The retrieval of soil properties in immediate post-fire conditions featured a higher overall fit and predictive capacity from ALOS-2 L-band SAR backscatter data than from Sentinel-1 C-band SAR data, with the absence of noticeable under and overestimation effects. The transferability of the ALOS-2 based model to one year after wildfire exhibited similar performance to that of the model calibration scenario (immediate post-fire conditions). Soil organic carbon and available phosphorous content was significantly higher one year after wildfire than immediately after the fire disturbance. Conversely, the short-term change in soil total nitrogen was ecosystem-dependent. Our results support the applicability of L-band SAR backscatter data for monitoring short-term variability of fire effects on soil properties, reducing data gathering costs within large and heterogeneous burned landscapes SI

A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R

Remote sensing-based approaches are important for evaluating ecosystem degradation and the efficient planning of ecosystem restoration efforts. However, the large majority of remote sensing-based degradation assessments are trend-based, implying that they can only detect degradation that occurred after medium or high-resolution satellite imagery became available. This makes them less suitable to map long-term degradation in ecosystems that have been under high human pressure since before. The main goal of this study was to develop a robust operational approach to map forest degradation status in heterogeneous landscapes with a long-standing degradation history to inform the planning of restoration interventions. We hereby use the tropical dry forests of Lambayeque, Peru, as a case study. Instead of using a trend-based assessment, we evaluated forest degradation status by comparing current woody cover (WC) and aboveground biomass (AGB) estimates obtained from remote sensing imagery with benchmark values consisting of the 95th percentile WC and AGB values inside environmentally homogenous land capability classes. Using boosted regression tree models and a combination of optical (Sentinel-2) and synthetic aperture radar (Sentinel-1) data of different seasons, we mapped WC and AGB, using training data obtained through very high-resolution imagery and field measurements. Further, we aimed at assessing (i) whether the inclusion of Sentinel-1 data improves mapping accuracy in comparison to using only Sentinel-2 data, and (ii) whether the use of multi-seasonal data improves accuracy in comparison to single-season data. Models combining multi-seasonal Sentinel-1 and Sentinel-2 data resulted in the most accurate WC predictions (mean absolute error (MAE): 16%; MAE normalized by dividing by the inter-quartile range of training data: 26%) and AGB predictions (MAE: 28.6 t/ha; normalized MAE: 65%), but differences in predictive accuracy with single season models or models using only Sentinel-2 data were small. The most accurate models estimated an average WC of 41% and an average AGB of 23.4 t/ha. Average WC and AGB reduction due to degradation was 35% and 36%, respectively, indicating that these forests are highly degraded. The site-specific scaling of WC and AGB allows to efficiently estimate forest degradation status irrespective of the time when this degradation occurred, and to express degradation status against site-specific benchmarks. On the condition that there are still some areas that are sufficiently undegraded to be used as a benchmark, the approach can be used to prioritize forest restoration actions and inform targets for restoration in heterogeneous landscapes suffering the impacts of undocumented long-term degradation.

PURPOSE/OBJECTIVE(S) To evaluate the cumulative dose to organs at risk (OAR) for Glioblastoma multiforme (GBM) patients with multiple treatment courses via a purpose-developed RAdiotherapy Dose Accumulation Routine (RADAR), and to compare the results with manual estimations from the special medical physics consult (SPC). MATERIALS/METHODS RADAR as a treatment planning system plugin was created to automatically transfer dose from previous radiation treatment plans to the current planning CT based on approved rigid (RR) or deformable image registration (DIR), convert the physical dose in each voxel to equivalent dose in 2Gy fractions (EQD2), and subsequently accumulate EQD2 doses from all relevant treatments. RADAR provides a novel option to take the registration and contour uncertainty into considerations by conservatively replacing dose to a given OAR voxel with a local maximum dose searched within an ellipsoid of the voxel. Ten GBM cases treated with 2 or more radiation courses between 2017 to 2020 were selected to analyze the registration/contour uncertainties and compare OAR dose from SPC and RADAR retrospectively. Brainstem, optic chiasm, and both optic nerves on previous treatment scans were propagated to the current CT scan based on RR and DIR. For each structure, mean distance to agreement (MDA) was measured between propagated previous and current OAR contours, and used for assigning search radius of ellipsoids accounting for uncertainties. An α/β of 2 for OARs and 10 for gross tumor volume were used for EQD2 conversion. The maximum dose (Dmax) and dose to 5% volumes (D05) from the accumulated EQD2 dose was evaluated based on the structures in the most recent plan and compared to SPC estimated dose. The SPC dose was documented by adding dose reported to the OAR based on the structure set in each of the previous plans without considering dose location. RESULTS The mean, standard deviation of the MDA of brainstem, optic chiasm, left and right optic nerve were used to determine a search radius of 2.0 mm, 2.5 mm, 2.5 mm, and 2.5 mm for these OAR, respectively. Comparing the manually estimated EQD2 dose from SPC and RADAR, the dose differences of -23Gy up to 40 Gy were found. RADAR used current structure set for OAR evaluation whereas SPC used the structure set from each course. Inconsistencies in contouring can be attributed to the large dose difference for some cases. For 5 of 10 test cases, the RADAR-derived OARs dose was higher than departmental guidelines and would potentially trigger a re-plan. CONCLUSION RADAR is capable to automatically convert the physical dose to EQD2 dose with various treatment courses by rigid or deformable image registration, take into account the registration and contour uncertainties and evaluate accumulated dose more accurately and efficiently than manual estimation. RADAR improves the workflow of decision making when optimizing plans with multiple previous treatments.

This paper revisits the problem of multiple parameters estimation for coherent targets in bistatic EMVS-MIMO radar. By extending the spatial smoothing mechanism to the EMVS-MIMO radar, three generalized spatial smoothing estimators, named the TS approach, the RS approach and the TRS approach, have been proposed. The killer idea of the proposed methodologies is to recover the rank of the covariance matrix via averaging the array measurement in spatial domain, and then estimate the parameters from the cooperation of the normalized vector cross-product technique and the LS method. Unlike the state-of-the-art polarization smoothing methods, the proposed methods would not sacrifice the polarization information, so that TS, RS and TRS are capable of providing polarization information of the coherent targets. The proposed estimators do not require any constrain on the geometries of the Tx/Rx EMVS arrays. They are computationally friendly yet retain robustness to the sensor position error. The proposed estimators are analyzed in detail, and numerical simulations are provided to verify their theoretical advantages.

In this paper, we propose a novel density-based radar scanning clustering algorithm. Its main objective is to quickly discover and accurately extract individual clusters by employing the radar scanning strategy. By using this algorithm, the number of clusters does not need to be specified beforehand. Two techniques are utilized in our proposed method. First, we use a fast mean-shift algorithm with adaptive radius and active subsets to effectively locate the centers, reducing the computational time significantly. Second, we employ the shape of the probability density function of the distribution of distances between a selected point and the other points in the data set. This is performed to determine the critical parameters of the radiuses of the fast mean-shift algorithm and radiuses of clusters. The new algorithm has four merits. It reduces the computational complexity, overcomes problems caused by high dimensionality, is capable of dealing with heterogeneous spherical data sets, and lastly, is robust to noise and outliers. After applying our proposed method to several kinds of synthetic and real-world data sets, the results indicate that the density-based radar scanning algorithm is efficient and accurate.

Radar sounders are becoming essential methods for the exploration of the Earth, Planets and the interior of small Solar System bodies. The performance of a radar is strongly related to the electromagnetic parameters of the materials composing the body surface and subsurface. Given the very limited access to planetary soil and rocks, meteorites represent important analogues of asteroids and Jovian icy moon crusts. Electromagnetic properties of meteorites are poorly known as they have seldom been investigated through extensive laboratory measurements as a function of frequency, temperature, and density. The present work builds upon a previous study on a solid chondrite sample, extending the measurements to the complex dielectric permittivity and magnetic permeability of a crushed L5 chondrite in the frequency range typical of the radars proposed for future space missions, such as AIDA, JUICE and EUROPA CLIPPER. The results show that for such dry granular materials the main parameter controlling the electromagnetic properties is the sample bulk density. Moreover, our results highlight the importance to account for magnetic properties in radar signal attenuation estimation for planetary exploration.

Limited attention is typically paid to the cause-effect relationship between land subsidence due to aquifers overexploitation in expanding metropolises and urban growth models and patterns. This paper implements an integrated urban and satellite Interferometric Synthetic Aperture Radar (InSAR) approach to investigate subsidence, multi-decadal urban growth and peopling trends in the Metropolitan Area of Morelia (ZMM) in the Mexican state of Michoacán. Stacking of JRC's Global Human Settlement Layer, DLR's World Settlement Footprint and INEGI's National Geostatistical Framework datasets reveals a predominant edge-expansion growth model, with urban densification in 1975-2020 and some sprawling in 1990-2000. Population of the ZMM doubled in the last 30 years, reaching over 1 million inhabitants. The ENVISAT and Sentinel-1 InSAR analysis confirms that subsidence is structurally-controlled by the main normal faults within the Cuitzeo half-graben. Differential sinking and ground discontinuities are aligned with buried tectonic faults and contrasting compressible sediment thickness. Non-linearly deforming subsidence bowls develop at extraction wells in both old and newly urbanized sectors of the ZMM. Maximum vertical displacement velocities increased from -2.5 cm/year in 2003-2010 to -9.0 cm/year in 2014-2021, with subsidence migrating towards recently urbanized zones. More than 250 new groundwater wells were added to the public registry since 2000, many of which within new urban sectors. Time-lapse InSAR reveals a 4 km2 rapidly subsiding bowl that formed at the largest social housing neighbourhood of Villas del Pedregal, as building lots were progressively completed and sold, and new wells registered. With angular distortions due to the differential subsidence reaching 0.12% in 2014-2021, new buildings and roads are exposed to fracturing and surface faulting risk of comparable level as the city historic building blocks located along the main faults. By providing useful insights into the relationship between urban growth and land subsidence in the ZMM, the approach proves valuable for application to other metropolises worldwide.

Fresh water stored by on-farm reservoirs (OFRs) is an important component of surface hydrology and is critical for meeting global irrigation needs. Farmers use OFRs to store water during the wet season and for crop irrigation during the dry season, yet their seasonal and inter-annual variability and downstream impacts are not quantified. Therefore, OFRs' sub-weekly surface area changes are critical to understanding their dynamics and mitigating their downstream impacts. However, prior to the recent increase in satellite imagery availability and improvement in sensors' spatial resolution, monitoring the OFRs' sub-weekly surface area changes across space and time was challenging because OFRs occur in high numbers (i.e. hundreds) and are small water bodies ( 0.95) and small uncertainties (4–8%) when compared to the validation dataset. We found higher uncertainties (5–14%) when adding S1 to the Kalman filter—this is related to the higher uncertainties found for S1 (~20%). The algorithm can assimilate optical and radar satellite data to increase the OFRs' surface area time series cadence allowing us to investigate sub-weekly surface area changes. The algorithm is not sensor-specific, and it accounts for the uncertainties in both the sensors observations and the resulting surface areas, which are key advantages when compared to other algorithms used to combine satellite data. By improving the surface area observations cadence and providing the surface area uncertainties, the approach presented in this study has the potential to enhance water conservation plans by allowing better assessment and management of the OFRs.

This article develops a collaborative radar node selection and transmitter resource allocation (CRNS-TRA) algorithm for target tracking applications in multiple radar architectures. The key mechanism of the CRNS-TRA strategy is to coordinate the radar node selection, transmit power, and dwell time of multiple radar architectures to enhance the target tracking accuracy under the constraints of several transmitter resource budgets. The analytical expression for the Bayesian Cramer-Rao lower bound (BCRLB) is derived and employed as the objective function to quantify the target tracking performance. It is shown that the CRNS-TRA problem is a mixed-integer, non-convex, and non-linear optimization problem, where the three involved parameters, that is, the radar node selection, transmit power and dwell time allocation, are all coupled in the objective function and the constraints. Subsequently, a fast and effective two-stage-based solution methodology is proposed for solving the resulting problem, which incorporates the interior point method and cyclic minimization framework. Extensive simulation results are provided to verify that the presented CRNS-TRA scheme is capable of effectively improving the target tracking performance with much lower operation time consumption when compared to other state-of-the-art resource allocation benchmarks.

Mountain formation and evolution on Titan is poorly understood, due in part to a lack of high-resolution topographic data. By applying advanced processing techniques, we are able to increase the along-track spatial resolution of the Cassini RADAR altimeter by up to a factor of ten, enabling more detailed analysis. A survey of mountainous and hummocky terrain reveals a unique, characteristic waveform shape. By modeling reflections from different landscapes, we are able to show that these waveforms contain sub-resolution topographical information. We found that mountain elevation on Titan varies greatly over short distances, and evidence suggests that many mountain ranges on Titan have been eroded down to the plains level even within a single high-resolution radar footprint.

Inadvertent use of medication that has been tampered with can cause serious harm. Monitoring how and when medication was last used or touched is important for mitigating risks. In this paper, we present a new radar-based monitoring system that can detect eight different types of tampering methods with three types of medication containers. Our system works by using a FMCW and CW Doppler radar to capture motion speed, direction, and range, which we use for classifying activities. For monitoring activities at home, our system can be setup underneath a kitchen cabinet to monitor medication left out on the countertop. As our system uses radar, we can preserve privacy of individuals as the signatures from the radar are specific to the locations of the antennas and not necessarily associated with an individual. For classifying activities we created a processing pipeline that extracts a set of features from the raw multivariate time series signals from the radar. We then used three types of data augmentation techniques including jittering, scaling, and magnitude warping, to increase our data sets and increase our classification model accuracy. In addition, we evaluated our system using 5-fold cross validation and with different types of augmentation data sets. Our system can achieve 99% accuracy using a logistic regression classifier with multiple people.

Elliptically-contoured distributions (ECD) play a significant role, in computer vision, image processing, radar, and biomedical signal processing. Maximum likelihood estimation (MLE) of ECD leads to a system of non-linear equations, most-often addressed using fixed-point (FP) methods. Unfortunately, the computation time required for these methods is unacceptably long, for large-scale or high-dimensional datasets. To overcome this difficulty, the present work introduces a Riemannian optimisation method, the information stochastic gradient (ISG). The ISG is an online (recursive) method, which achieves the same performance as MLE, for large-scale datasets, while requiring modest memory and time resources. To develop the ISG method, the Riemannian information gradient is derived taking into account the product manifold associated to the underlying parameter space of the ECD. From this information gradient definition, we define also, the information deterministic gradient (IDG), an offline (batch) method, which is an alternative, for moderate-sized datasets. The present work formulates these two methods, and demonstrates their performance through numerical simulations. Two applications, to image re-colorization, and to texture classification, are also worked out.

The cloud vertical structure (CVS) over any place significantly impacts the boundary layer dynamics and horizontal/vertical temperature gradient at local scale, whereas on the synoptic scale it plays a crucial role in determining the atmospheric circulation and hydrological cycle. There is a notable lack of long-term comprehensive studies on CVS over Indian subcontinent. Here, we have done a comprehensive study of CVS over the Indo-Gangetic plain (IGP), in which we have studied the statistics of CVS over various urban agglomerations (UAs) lying within the IGP (using radiosonde data) coupled with CVS of the entire IGP (EIGP) which majorly includes rural and suburban areas (using CloudSat profiling radar data) from 2006 to 2017. Seasonal analysis shows that CVS is much different in monsoon season as compared to other seasons over both EIGP and UAs. The frequency of single layer clouds is ~91%, 87%, 76% and 91% over EIGP and ~ 81%, 80%, 58% and 82% over UAs in winter, pre-monsoon, monsoon and post-monsoon seasons, respectively. In the monsoon season, both data show significant increase in multi-layer clouds frequency, however, the frequency of multi-layer clouds is significantly higher over UAs (>40%) compared to that over EIGP (

Sinkhole activity in Florida is a major hazard for people and property. Its increasing frequency is often related to an accelerated use of ground-water and land resources in the region. In this work, we use a combined approach of radar interferometry and spatial clustering analysis over three selected sites in West-Central Florida to identify localized deformation that may be caused by sinkhole activity. The region of West-Central Florida is a densely active sinkhole region, where sinkholes tend to be small and land cover is mixed resulting in variable interferometric coherence that complicates Interferometric Synthetic Aperture Radar (InSAR) surveys. In this work, we present a combined methodology implementing InSAR and a Density-Based Spatial Clustering Analysis (DBSCAN) algorithm to detect unknown sinkhole activity and to alert to possible precursors of sinkhole collapse. The data used for the study consist of acquisitions from three TerraSAR-X frames covering time spans of ~1.7 and 2.5 years with spatial resolutions ranging from 25 cm up to 1 m. We applied the Persistent Scatterer Interferometry (PSI) technique using the Stanford Method for Persistent Scatterers (StaMPS) and confirmed the observed deformation signals by also processing the data using the SAR PROcessing tool (SARPROZ). Results show several areas of localized subsidence, from which the cluster with highest rates for each site was selected for detailed inspection. Locations of selected clusters were found in buildings with sizes ranging from 300 m2 to nearly 2000 m2, with subsidence trends ranging from −3 to −6 mm/yr. Results were compared with in-situ observations such as ground penetrating radar (GPR), electrical resistivity tomography (ERT) surveys, visual structural inspection and public county archive documents to help as ground truthing; subsiding locations were found to be related to sinkhole presence or development.

This study presents results on the internal structure and recent dynamics of a thrust moraine complex in the Zay Valley (Italian Alps) by means of geophysical surveying and remote sensing time series. Results from electrical resistivity tomography, refraction seismic tomography, ground-penetrating radar as well as a four-phase modeling approach provided detailed information on the internal moraine structure and confirmed the presence of partly ice-rich permafrost within the moraine complex as well as buried massive ice of sedimentary origin within the proximal moraine flank. The use of Synthetic Aperture Radar interferometry (InSAR) enabled the derivation of detailed surface displacement patterns showing subsidence rates of up to 5 cm within a 30-day period (August/September 2020) over the central part of the moraine complex. Further, preliminary InSAR results indicated an eastward displacement back towards the glacier forefield. The comparison of the results from geophysical measurements and InSAR time series suggested a strong relationship between the subsurface conditions and the surface displacement, as highest displacement rates were found over locations with highest ice content. The consistency of the results enables an area wide estimation of subsurface conditions and highlights the benefits from combining geophysical and remote sensing methods.

In this paper, a new depth-resolved tomography approach titled laser induced chirp-pulsed radar photothermal tomography (CR-PT) is introduced, and it is employed for the 3D visualization detection for carbon fiber reinforced polymer (CFRP). Initially, the time delay correlation dual orthogonal demodulation (DOD) algorithm is applied to extract the time/frequency-domain characteristic sequence of the chirp-pulsed radar signal, and the applicability of CR-PT is analyzed by COMSOL simulation environment. Subsequently, the correctness of the simulation analysis is verified by the experiment of detecting CFRP with the flat bottom holes by CR-PT. Meanwhile, the accuracy of the shape, position, and depth of the defects detected by CR-PT is determined. Finally, the immersion ultrasonic C-scan imaging and CR-PT are employed to compare and analyze the delamination defect of CFRP composites, which proved the superiority of CR-PT. The experimental results illustrated that CR-PT technique can achieve high-quality 3D geometric reconstruction of CFRP delamination defect, which provides a new attempt and method for quantitative non-destructive evaluation and tomography of CFRP composites.

Furious floods have become an omnipresent reality with the dawn of climate change and its transition to adulthood. Since climate change has now become an accepted reality, analysing the factors that favour or disfavour floods are an urgent requirement. Here we showcase the role of paleochannels, a product of migrating rivers, in a catastrophic flood in the south-western part of the Indian Peninsula. This study exposes whether these geomorphic features facilitate or impede floods. For the purpose of extracting paleochannels and floodwater mapping, we utilized multiple satellite datasets and took advantage of diversified feature selection algorithms. Paleochannels were demarcated viz., initial identification of a few paleochannels from literature and confirmation through high-resolution Google Earth (GE) images, followed by Principal Component Analysis (PCA) of Sentinel-2 images using Google Earth Engine (GEE), and a supervised classification of the principal bands 1, 2, and 3. False-positives were eliminated using Object-Oriented Analysis (OOA), which reduced the 964,254 polygons to 23,254. These polygons were visually affirmed using GE images that resulted in 115 paleochannels as the final collection. A few locations were verified through Vertical Electrical Sounding (VES) using the Schlumberger method. The features were analysed with the floodwaters of the 2018 catastrophic flood, extracted from Synthetic Aperture Radar (SAR) data, which was delineated for different temporal limits including the day of peak flood of August 17, 2018. During the peak flood, the inundation of the study area extended to 534.86 km2 with all the paleochannels getting immersed in floodwater. After 44 days of peak flood, the post-flood analysis revealed that when the floodwater receded 50%, the paleochannels emptied 87.39%, with the midland paleochannels discharging more than those of lowlands. Thus, such geomorphic features can be flood hotspots, but can be considered for discharging floodwater to mitigate flood risk in case of unprecedented rain.

In the development of Sanya Incoherent Scatter Radar (SYISR), which is a major scientific instrument project founded by the National Natural Science Foundation of China (NSFC), we established a prototype system of 8 subarrays (named SYISR-8 hereafter), including 256 transceiver modules totally, beginning in July 2018 to verify the functions of the radar. Based on SYISR-8, we configured the system to detect the meteor echo with high range resolution. During a continuous observation interval of 16 h, 55 meteor echo events were found. Among these events, 34 cases were head echoes, 18 cases were specular trail echoes, and 3 cases were head echo and nonspecular trail echo concurrent events. We have observed 2 potential direct meteoroid fragmentation cases of all 55 meteor events. It demonstrates that potential direct observation of meteoroid fragmentation could be observed by a relatively small aperture and power using pulse compression processing. However, the occurrence ratio of meteoroid fragmentation in our experiment is approximately 6%, which is considerably lower than that observed in previous studies, which needs further investigation.

Data-linked Next Generation Weather Radar (NEXRAD) images can be delayed up to 20 min in the cockpit. Pilots' underappreciating or ignoring the time delay may be the major cause of two fatal accidents. No studies have connected spatial awareness with accidents. This study evaluated how delayed radar information affects the spatial awareness of pilots at three levels of analysis. Thirty-one student pilots and flight instructors completed three sequential estimation tasks (i.e., the current location of storms, the current relative distance to storms, and the future relative distance to storms). Fifty-four weather scenarios were developed for three factors (storm speeds, delays, displays) and presented to pilots. The results indicated that delays and the storm speed significantly affected the three levels of spatial awareness. Participants' estimation accuracy was the lowest under long delay and fast speed in the current location estimation, under medium delay and speed in the current distance estimation, and under short delay and slow speed in the future distance estimation. Spatial awareness could be high under the long delay and fast speed conditions if pilots had no time limits. Thus, pilots can process 20-min delayed radar information. However, there were no differences in estimation accuracy between the static and animation displays in any of the conditions. Well-designed features on displays, such as scale or distance measuring tools, can aid pilots’ spatial estimation and support all levels of spatial awareness.

Direct sensing of total ocean surface currents with microwave Doppler signals is a growing topic of interest for oceanography, with relevance to several new ocean mission concepts proposed in recent years. Among those, WaCM (Rodriguez et al., 2019), SKIM (Ardhuin et al., 2019) and the ESA Earth Explorer candidate Harmony (ESA, 2020) and SEASTAR (Gommenginger et al., 2019).Since 2014, the spaceborne C-band SAR instruments of the Copernicus Sentinel-1 (S1) mission routinely acquire microwave Doppler data, distributed to users through operational S1 Level-2 ocean radial velocity (L2 OCN RVL) products. S1 L2 RVL data could produce high-resolution maps of ocean surface currents that would benefit ocean observing and modelling, particularly in coastal regions. However, uncorrected platform effects and instrument anomalies continue to impact S1 RVL data and prevent direct exploitation.In this paper, a simple empirical method is proposed to calibrate and correct operational S1 L2 RVL products and retrieve two-dimensional maps of surface currents in the radar line-of-sight. The study focuses on the German Bight where wind, wave and current data from marine stations and an HF radar instrumented site provide comprehensive means to evaluate S1 retrieved currents. Analyses are deliberately limited to Sentinel-1A (S1A) ascending passes to focus on one single instrument and fixed SAR viewing geometry. The final dataset comprises 78 separate S1A acquisitions over 2.5 years, of which 56 are matched with collocated HF radar data. The empirical corrections bring significant improvements to S1A RVL data, producing higher quality estimates and much better agreement with HF radar radial currents.Comparative evaluation of S1A against HF radar currents for different WASV corrections reveal that best results are obtained in this region when computing the WASV with sea state rather than wind vector input. Accounting for sea state produces S1 radial currents with a precision (std of the difference) around 0.3 m/s at ~1 km resolution. Precision improves to ~0.24 m/s when averaging over 21 × 27 km2, with correlations with HF radar data reaching up to 0.93. Evidence of wind-current interactions when tides and wind align and short fetch conditions call for further research with more satellite data and other sites to better understand and correct the WASV in coastal regions.Finally, 1 km resolution maps of climatological S1A radial currents obtained over 2.5 years reveal strong coastal jets and fine scale details of the coastal circulation that closely match the known bathymetry and deep- water coastal channels in this region. The wealth of oceanographic information in corrected S1 RVL data is encouraging for Doppler oceanography from space and its application to observing small scale ocean dynamics, atmosphere and ocean vertical exchanges and marine ecosystem response to environmental change.

This paper presented a novel design of thin and wide-band electromagnetic absorber. The proposed structure was composed of a bottom metal plate, a homogeneous resistance layer (HRL), a dielectric spacer, and a periodic resistance layer (PRL) at the top made of the Jerusalem cross structure, and the overall thickness of the absorber was only 5.055 mm. The simulation result exhibited that more than 90% absorption has been achieved in 2–40 GHz, corresponding to the fractional bandwidth of 181% under the normal incidence. The measured and the simulated results showed an agreement. The absorber had a good potential application in the field of microwave absorption.

In order to address the range walk (RW) effect in the field of radar high speed target detection, the axis rotation MTD (AR-MTD) class algorithms based on long-time coherent integration, i.e., AR-MTD, improved axis rotation MTD (IAR-MTD), and modified location rotation transform (MLRT) have been proposed in recent works. However, they are faced with detection performance loss and large computational complexity. In this paper, a new axis rotation MTD (NAR-MTD) algorithm is proposed. Different from the previous AR-MTD class algorithms, NAR-MTD first selects the image center of 2-D compressed signal as the coordinate system origin, and then simplifies the axis rotation relationship to reduce the rotation error caused by the coordinate transformation and correct the RW to the initial range location. Furthermore, by exploiting the geometrical information of mismatched NAR-MTD, the adaptive AR-MTD (AAR-MTD) is proposed to realize efficient motion parameter estimation of the moving target in different SNR environments. Numerical simulations and real data process indicate that NAR-MTD has a better detection ability than that of AR-MTD class algorithms. Meanwhile, the computational efficiency of AAR-MTD is about two orders of magnitude higher than that of direct 2-D search of NAR-MTD.

The accuracy of target position and velocity measurement is one of the most concerned indexes of Distributed High Frequency Surface Wave Radar (HFSWR). The target localization of HFSWR is commonly achieved by indirect positioning approach. Recently, some direct positioning methods using the raw signals collected on all base-stations have been studied, which have superior positioning performance compared with indirect approaches. But there is almost no joint estimation method for target position and vector-velocity. In this paper, a novel direct target localization and vector-velocity measurement method based on bandwidth synthesis (BS-DTL) in distributed HFSWR system is investigated to improve the accuracy of the target. BS-DTL algorithm that makes full use of the information of all base-stations is considered for the joint estimation of target position and vector-velocity. This paper studies the detection and estimation performance of the BS-DTL algorithm and traditional direct target localization method (DTL). Numerical simulation results show that the proposed algorithm outperforms the traditional DTL method and the position and vector-velocity can be accurately estimated.

Glaciers in polar regions are sensitive to climate and ocean changes and can thin rapidly as a consequence of global warming. Digital Elevation Models (DEMs) from remote sensing observations have been widely used to detect changes in polar glaciers. DEMs from Terrestrial Radar Interferometer (TRI) have recently been used for high frequency glacier change and glacier-ocean interaction studies. However, it is unclear whether TRI DEM over a large study area can be combined directly with remote sensing observations to investigate glacier changes as well as the accuracy of TRI DEM at far range. In this study, we deployed a TRI close to Helheim Glacier, East Greenland and generated DEMs using TRI and satellite laser altimetry. We analyzed the accuracy of the TRI DEM using theoretical calculations, comparisons based on repeat observations, and comparisons with a high accurate ArcticDEM. The validation results suggest that for stable ground surfaces, the uncertainty (standard deviation) is

In this paper, delay and Doppler parameters of multiple moving targets are estimated by exploring Compressed Sensing (CS) which enables the reconstruction of a sparse signal from a small set of measurements. No Side lobes appear with CS and hence no false alarms can occur. Also closely spaced targets could be detected with high resolution than traditional Matched Filter (MF). Deterministic DFT measurement matrix is employed, that is multiplied with the received echo signal and down sampled to obtain compressed measurements. For this deterministic sub-sampling is done by choosing first m rows rather than random selection of rows (random sub-sampling), so that matrix can be regenerated during reconstruction without the necessity for storage. Reconstruction algorithm Step Wise regression using False Discovery Rate (SWFDR) stopping rule could effectively optimize noise that leads to reduction in storage space and high rate ADC requirement drastically even in the presence of noise.