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Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services. [ABSTRACT FROM AUTHOR]

SAR images are used in many applications such as building detection. Extracting the building is very challenging due to the radar nature of the SAR images. However, due to the advantages of radar images such as day and night imaging, building extraction from SAR images is a hot topic. In this context, one of the main challenges is the effect of building orientation on the profile created in the SAR image. Also, the two geometric distortions of shadow and layover affect the SAR image. In most building extraction methods, shadow and double bounce are used as two main parameters in building detection. In this paper, different morphological profiles for detecting shadow index and double-bounce index (DMPSIDI) method have been developed using its combination with the method based on statistical features for building extraction. The DMPSIDI method is a morphological-based method that extracts buildings from SAR images independent of changing their profile. The proposed method is also robust to different data using weighting in the main parameters of shadow and double bounce. [ABSTRACT FROM AUTHOR]

Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status. [ABSTRACT FROM AUTHOR]

Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Special attention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. [ABSTRACT FROM AUTHOR]

Abstract LabOSat (acronym for "Laboratory On a Satellite") is a type of electronic platform designed to perform experiments in harsh, remote environments. Up to now, LabOSat platforms have been used mainly for characterizing and validating custom and commercial electronic devices at Low Earth Orbits (LEO). Many of these platforms are running experiments aboard ÑuSat satellites, which are built and operated by Argentine company Satellogic. A brief description of LabOSat platforms is presented here, along with details of their validation both at LEO and at a nuclear reactor. The electrical characterization of custom non-volatile memory cells — as performed by a LabOSat platform at LEO — is also described, and the results of such experiment are compared with results obtained on Earth. The memory cells studied were TiO 2 -based and La 1/3 Ca 2/3 MnO 3 -based ReRAM devices. Highlights • LabOSat, a characterization platform, was tested under radiation environments. • Satellite payload: excellent performance at Low Earth Orbits (LEO) in a 3-year mission. • LabOSat platform was used to study ReRAM cells on board microsatellites. • Measurements on ReRAM cells at LEO are in agreement with on-Earth measurements. [ABSTRACT FROM AUTHOR]

In this paper, we provide a review of the different approaches used to detect activities inside a building using through-the-wall radar. We classify two main types of radar that are used in detection activities behind the wall. Stepped frequency and pulse compression radar are the most frequently used types of radar in through-the-wall radar imaging (TWRI). In addition, a preference has been shown for using frequencies in the range of 1-3 GHz. Though a high volume of research is being done in this area, there are still many open issues. Detectability of the human being, wall modeling, and target differentiation have been identified as the main open issues in the field of TWRI. [ABSTRACT FROM AUTHOR]

In this paper, monitoring of metal-cased oil wells using the ultrawideband (UWB) radar is proposed. The inspection includes the detection and imaging of perforations and corroded areas in a metal pipe. Detection of small anomalies/ perforations on the surface of a narrow metal pipe is very challenging. Here, we present a method for imaging such small anomalies based on the extra time delay of the reflected pulse due to the effect of perforation in the radar near field. In this paper, the necessary concepts for the use of UWB radar specified for this application are developed and proved based on different measurement and simulation scenarios. We have experimentally demonstrated the effect of the perforations’ size on the time delay of reflected pulses. The distance between the perforation and the radar, for the near-field phenomenon, is critical for an effective detection and imaging. Therefore, we also studied the optimal distance between the radar and the perforation. Perforations with a size range of 1–3 cm are considered for the experiments and simulations. The experiments are done both in air and diesel. Synthetic aperture radar processing is used to reconstruct the images of the perforations and corroded area. Measurement and simulation results demonstrate the potential of UWB radar systems for oil well monitoring applications. [ABSTRACT FROM AUTHOR]

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high-resolution image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus peltatus, Callitriche obtusangula, Potamogeton natans L., Sparganium emersum R. and Potamogeton crispus L., were classified from the data using object-based image analysis and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image resulted in 53% overall accuracy. These consistent results not only show promise for species-level mapping in such biodiverse environments but also prompt a discussion on assessment of classification accuracy. [ABSTRACT FROM AUTHOR]

We present observations obtained with the Middle Atmosphere Alomar Radar System (MAARSY) to investigate short-period wave-like features using polar mesospheric summer echoes (PMSEs) as a tracer for the neutral dynamics. We conducted a multibeam experiment including 67 different beam directions during a 9-day campaign in June 2013. We identified two Kelvin–Helmholtz instability (KHI) events from the signal morphology of PMSE. The MAARSY observations are complemented by collocated meteor radar wind data to determine the mesoscale gravity wave activity and the vertical structure of the wind field above the PMSE. The KHIs occurred in a strong shear flow with Richardson numbers Ri < 0.25. In addition, we observed 15 wave-like events in our MAARSY multibeam observations applying a sophisticated decomposition of the radial velocity measurements using volume velocity processing. We retrieved the horizontal wavelength, intrinsic frequency, propagation direction, and phase speed from the horizontally resolved wind variability for 15 events. These events showed horizontal wavelengths between 20 and 40 km, vertical wavelengths between 5 and 10 km, and rather high intrinsic phase speeds between 45 and 85 m s−1 with intrinsic periods of 5–10 min. [ABSTRACT FROM AUTHOR]

The commonly applied K-distribution to model the synthetic aperture radar image amplitude of the heterogeneous (non-Gaussian) sea surface as the basis for vessel detection has shown deficiencies in practical cases, particularly for spacebased systems. Due to a deviation between the K-probability density function and measured histograms in the tails, even the inclusion of thermal noise is oftentimes not sufficient to cover the range of environments that are expected. As a consequence, virtually all detectors try to reduce the large number of obtained false detections by relying on rather heuristic postprocessing steps. Consequently, they forfeit the crucial property of a constant false alarm rate. This paper proposes a novel statistical sea clutter model that describes the data more accurately, especially in challenging environments and thermal-noise limited cases. This new model stands out through its numerical simplicity, permitting efficient parameter adaptation thereby enhancing robustness and reducing computational complexity. Accordingly, the presented sea data model has the potential to replace the widely adopted K-distribution as model of choice for future operational applications. [ABSTRACT FROM AUTHOR]

This paper presents a method for the remote interrogation of batteryless humidity sensors based on statistical estimations and 3-D beam scanning techniques. As a proof-of-concept demonstration, a Van-Atta reflectarray sensor is interrogated with a 24-GHz frequency-modulated continuous-wave transceiver at various reading ranges. The extension of the proposed approach to passive or moving sensors is reported, and the measurement results are discussed. The relative humidity (RH) is derived from the statistical analysis of multidimensional radar echoes, while method is proposed to detect the sensor among clutter without knowing its exact position. Moreover, it is demonstrated that the cross-polarization effect of the Van-Atta reflectarray combined with the 3-D beam scanning technique allows the indoor measurement of the RH up to 58 m. [ABSTRACT FROM PUBLISHER]

Radar tracking of aircraft targets at low elevation angles can be complicated by clutter from wind turbines, which we define to be clutter. In this paper, we study detection in a staring pulse-Doppler radar. By exploiting the second-order correlation structure of this wind-turbine clutter we derive a target detector that uses a sequence of adaptive coherence scores. The detector uses a multipulse coherence statistic consisting of an incoherent geometric average of coherently computed adaptive coherence scores. The detector compares favorably to a multipulse coherence detector that uses no modeling or estimation of the clutter. [ABSTRACT FROM PUBLISHER]

In this paper, we propose the multiwindow Adaptive S-method (AS-method) distribution approach used in the time-frequency analysis for radar signals. Based on the results of orthogonal Hermite functions that have good time-frequency resolution, we vary the length of window to suppress the oscillating component caused by cross-terms. This method can bring a better compromise in the auto-terms concentration and cross-terms suppressing, which contributes to the multi-component signal separation. Finally, the effective micro signal is extracted by threshold segmentation and envelope extraction. To verify the proposed method, six states of motion are separated by a classifier of a support vector machine (SVM) trained to the extracted features. The trained SVM can detect a human subject with an accuracy of 95.4% for two cases without interference. [ABSTRACT FROM AUTHOR]

The point-to-area problemstrongly complicates the validation of satellite-based precipitation estimates, using surface-based point measurements. We simulate the limited spatial representation of light-to-moderate oceanic precipitation rates along ship tracks with respect to areal passive microwave satellite estimates using data from a subtropical island-based radar. The radar data serves to estimate the discrepancy between point-like and areal precipitation measurements. From the spatial discrepancy, two statistical adjustments are derived so that along-track precipitation ship data better represent areal precipitation estimates from satellite sensors. The first statistical adjustment uses the average duration of a precipitation event as seen along a ship track, and the second adjustment uses the median-normalized along-track precipitation rate. Both statistical adjustments combined reduce the root mean squared error by 0.24 mm h-1 (55%) compared to the unadjusted average track of 60 radar pixels in length corresponding to a typical ship speed of 24-34 km h-1 depending on track orientation. Beyond along-track averaging, the statistical adjustments represent an important step towards a more accurate validation of precipitation derived from passive microwave satellite sensors using point-like along-track surface precipitation reference data. [ABSTRACT FROM AUTHOR]

This paper reviews recent progress of portable short-range noncontact microwave radar systems for motion detection, positioning, and imaging applications. With the continuous advancements of modern semiconductor technologies and embedded computing, many functionalities that could only be achieved by bulky radar systems in the past are now integrated into portable devices with integrated circuit chips and printed circuits boards. These portable solutions are able to provide high motion detection sensitivity, excellent signal-to-noise ratio, and satisfactory range detection capability. Assisted by on-board signal processing algorithms, they can play important roles in various areas, such as health and elderly care, veterinary monitoring, human-computer interaction, structural monitoring, indoor tracking, and wind engineering. This paper reviews some system architectures and practical implementations for typical wireless sensing applications. It also discusses potential future developments for the next-generation portable smart radar systems. [ABSTRACT FROM AUTHOR]

Developments in microwave and millimeter-wave systems have enabled remote sensing techniques traditionally used in long-range applications to be employed in the relatively closer range applications of detection and classification of human presence and measurement of human properties. This paper reviews the techniques used in microwave remote sensing of humans and discusses prominent examples of experimental systems from the literature. Radar techniques have been developed for measuring the time-varying scattering returns from the various parts of the human body during motion, which are used for human presence detection and the classification of human activities. The effects of breathing and heartbeat are also detectable using Doppler radar techniques, which is applicable to medical applications as well as search-and-rescue and security. Recent developments in distributed radars have enabled the direct measurement of the angular velocity of moving people, and passive systems have also been developed which provide a complementary detection mode that can aid in detection and classification of humans. [ABSTRACT FROM AUTHOR]

The direct measurement of the angle rate of moving objects using a radar with a spatially diverse electric field pattern, a measurement analogous to the measurement of the range rate of a moving objects, represents, along with the measurements of range, range rate, and angle, a fourth basic radar measurement. Recently introduced and experimentally demonstrated, the theoretical accuracy of the direct measurement of angle rate is derived in this paper, and it is compared to the measurements of range, range rate, and angle in the context of the optimal signal forms for the best measurement accuracy. Signal forms achieving optimal accuracy for each measurement are discussed; example implementations of high-accuracy measurements are compared to the optimal forms; and the limitations of simultaneous measurements of pairs of measurements are derived. Combining the angle rate measurement with the three other standard radar measurements may provide future radar systems the capability to simultaneously and instantaneously measure the position and 3-D trajectory of moving objects without compromising the accuracy of any individual measurement. [ABSTRACT FROM AUTHOR]

Chirp signals arise in many applications of signal processing. In this paper, we address the problem of detection of chirp signals that are encountered in a bistatic radar which we are developing for remote sensing of cosmic ray induced air showers. The received echoes from the air showers are wideband and characterized by very short sweep periods. This makes our astrophysical problem a challenging one, since a very short sweep period is equivalent to a very low energy chirp signal. Furthermore, the related parameters of the received echoes are nondeterministic since they are tied to the physical parameters of the air showers. The primary focus of this research is to address these challenges and to find an optimized detection approach in the additive white Gaussian noise channel for low signal-to-noise ratio (SNR) levels. Matched filters are commonly used in the radar systems when the chirp signal is known. In this paper, we revisit this context and use a “matched filter” as a basis of building a rake-like receiver which consists of a set of filters matched to quantized chirp rates, logarithmically distributed within the chirp-rate interval of interest. We examine the detection capability of the proposed structure through extensive theoretical and numerical analysis. Theoretical analysis and simulation results prove that the proposed detector has high detection capability for a range of chirp slopes in a low-SNR environment. [ABSTRACT FROM AUTHOR]

Cloud electrification and related lightning activity in thunderstorms have their origin in the charge separation and resulting distribution of charged iced particles within the cloud. So far, the ice distribution within convective clouds has been investigated mainly by means of ground-based meteorological radars. In this paper we show how the products from Cloud Profiling Radar (CPR) on board CloudSat, a polar satellite of NASA's Earth System Science Pathfinder (ESSP), can be used to obtain information from space on the vertical distribution of ice particles and ice content and relate them to the lightning activity. The analysis has been carried out, focusing on 12 convective events over Italy that crossed CloudSat overpasses during significant lightning activity. The CPR products considered here are the vertical profiles of cloud ice water content (IWC) and the effective radius (ER) of ice particles, which are compared with the number of strokes as measured by a ground lightning network (LINET). Results show a strong correlation between the number of strokes and the vertical distribution of ice particles as depicted by the 94 GHz CPR products: in particular, cloud upper and middle levels, high IWC content and relatively high ER seem to be favourable contributory causes for CG (cloud to ground) stroke occurrence. [ABSTRACT FROM AUTHOR]

Radar is a very important sensor in surveillance applications. Near-space vehicle-borne radar (NSVBR) is a novel installation of a radar system, which offers many benefits, like being highly suited to the remote sensing of extremely large areas, having a rapidly deployable capability and having low vulnerability to electronic countermeasures. Unfortunately, a target detection challenge arises because of complicated scenarios, such as nuclear blackout, rain attenuation, etc. In these cases, extra care is needed to evaluate the detection performance in blackout situations, since this a classical problem along with the application of an NSVBR. However, the existing evaluation measures are the probability of detection and the receiver operating curve (ROC),which cannot offer detailed information in such a complicated application. This work focuses on such requirements. We first investigate the effect of blackout on an electromagnetic wave. Performance evaluation indexes are then built: three evaluation indexes on the detection capability and two evaluation indexes on the robustness of the detection process. Simulation results show that the proposed measure will offer information on the detailed performance of detection. These measures are therefore very useful in detecting the target of interest in a remote sensing system and are helpful for both the NSVBR designers and users. [ABSTRACT FROM AUTHOR]

A new and robust constant false alarm rate (CFAR) detector based on truncated statistics (TSs) is proposed for ship detection in single-look intensity and multilook intensity synthetic aperture radar data. The approach is aimed at high-target-density situations such as busy shipping lines and crowded harbors, where the background statistics are estimated from potentially contaminated sea clutter samples. The CFAR detector uses truncation to exclude possible statistically interfering outliers and TSs to model the remaining background samples. The derived truncated statistic CFAR (TS-CFAR) algorithm does not require prior knowledge of the interfering targets. The TS-CFAR detector provides accurate background clutter modeling, a stable false alarm regulation property, and improved detection performance in high-target-density situations. [ABSTRACT FROM AUTHOR]

An image focusing method based on a realistic model for a wall is proposed for through-the-wall radar imaging using a multiple-input multiple-output array. A technique to estimate the wall parameters (i.e., position, thickness, and permittivity) from the radar returns is developed and tested. The estimated wall properties are used in the developed penetrating image formation to form images. The penetrating image formation developed is computationally efficient to realize real-time imaging, which does not depend on refraction points. The through-the-wall imaging method is validated on simulated and real data. It is shown that the proposed method provides high localization accuracy of targets concealed behind walls. [ABSTRACT FROM AUTHOR]

In this paper, a cancelling system for linear and three nonlinear frequency modulated signals is proposed. According to the RCS (Radar Cross Section) characteristic of target, amplitude and phase modulation are made to acquire the signal having the same frequency as the echo pulse but with the opposite phase. This signal cancels the radar echo signal. The basic theory of active cancellation stealth is introduced. Based on two kinds of radar target fluctuation models (Swerling I and Swerling III), the formulas of radar detection probability are given. The influence of amplitude, phase and frequency error on the radar detection probability have been discussed. Simulation results show that the technology of active cancellation stealth is feasible in theory, it could reduce the radar detection probability. [ABSTRACT FROM AUTHOR]

For advanced offshore engineering applications the prediction with available nautical X-band radars of phase-resolved incoming waves is very much desired. At present, such radars are already used to detect averaged characteristics of waves, such as the peak period, significant wave height, wave directions and currents. A deterministic prediction of individual waves in an area near the radar from remotely sensed spatial sea states needs a complete simulation scenario such as will be proposed here and illustrated for synthetic sea states and geometrically shadowed images as synthetic radar images. The slightly adjusted shadowed images are used in a dynamic averaging scenario as assimilation data for the ongoing dynamic simulation that evolves the waves towards the near-radar area where no information from the radar is available. The dynamic averaging and evolution scenario is rather robust, very efficient and produces qualitatively and quantitatively good results. For study cases of wind waves and multi-modal wind-swell seas, with a radar height of 5 times the significant wave height, the correlation between the simulated and the actual sea is found to be at least 90%; future waves can be predicted up to the physically maximal time horizon with an averaged correlation of more than 80%. [ABSTRACT FROM AUTHOR]

In this paper, two novel speed compensation algorithms for ISAR imaging under a low signal-to-noise ratio (SNR) condition have been proposed, which are based on the cubic phase function (CPF) and the integrated cubic phase function (ICPF), respectively. These two algorithms can estimate the speed of the target from the wideband radar echo directly, which breaks the limitation of speed measuring in a radar system. With the utilization of non-coherent accumulation, the ICPF-based speed compensation algorithm is robust to noise and can meet the requirement of speed compensation for ISAR imaging under a low SNR condition. Moreover, a fast searching implementation strategy, which consists of coarse search and precise search, has been introduced to decrease the computational burden of speed compensation based on CPF and ICPF. Experimental results based on radar data validate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In through-wall radar imaging (TWRI), ambiguities in wall characteristics including the thickness and the relative permittivity will distort the image and shift the imaged target position. To quickly and accurately estimate the wall parameters, an approach based on a support vector machine (SVM) is proposed. In TWRI problem, the nonlinearity is embodied in the relationship between backscatter data and the wall parameters, which can be obtained through the SVM training process. Measurement results reveal that once the training phase is completed, the technique only needs no more than one second to estimate wall parameters with acceptable errors. Then through-wall images are reconstructed using a back-projection (BP) algorithm by a finite-difference time-domain (FDTD) simulation. Noiseless and noisy measurements are discussed; the simulation results demonstrate that noisy contamination has little influence on the imaging quality. Furthermore, the feasibility and the validity are tested by a more realistic situation. The results show that high-quality and focused images are obtained regardless of the errors in the wall parameter estimates. [ABSTRACT FROM AUTHOR]

One pivotal factor affecting the accuracy of HF radar current measurements is the direction of arrival (DOA) estimation performance of the current signal. The beamforming technology or superresolution algorithm cannot always perform best in practical applications because of the phase errors existing in array channels. These phase errors, which cause uncertain estimation of DOA, lead to confused values in radial current maps. To solve this problem, this paper is focused on discussing the performances of two autocalibration methods using sea echoes for multifrequency high-frequency (MHF) radar current measurements. These two array calibration methods, based on maximum likelihood (ML) and multiple signal classification (MU), first seek single-DOA sea echoes and then gather them for array calibration using different cost functions. The ML and MU methods provide approximate mean phases, while the standard phase errors of the MU method are smaller. After array calibration using these two methods, the results show significant improvements in current retrievals. Comparisons between the MHF radar and ADCPs reveal that array calibration using the ML and MU methods also improves the estimation of radial currents clearly, with correlation coefficients over 0.93 and rms differences of 0.09-0.18 m s−1 at different operating frequencies and sampling locations. The performance of the bearing offset is also improved. Only small bearing offsets less than 10° exist in radial current measurements. Therefore, this paper demonstrates that array calibration is a crucial part for current measurements, especially for direction-finding HF radar. [ABSTRACT FROM AUTHOR]

A new attenuation correction method has been developed for the dual-frequency precipitation radar (DPR) on the core satellite of the Global Precipitation Measurement (GPM) mission. The new method is based on Hitschfeld and Bordan's attenuation correction method (HB method), but the relationship between the specific attenuation k and the effective radar reflectivity factor Z e ( k- Z e relationship) is modified by using the dual-frequency ratio (DFR) of Z e and the surface reference technique (SRT). Therefore, the new method is called the HB-DFR-SRT method (H-D-S method). The previous attenuation correction method, called the HB-DFR method (H-D method), results in an underestimation of precipitation rates for heavy precipitation, but the H-D-S method mitigates the negative bias by means of the SRT. When only a single-frequency measurement is available, the H-D-S method is identical to the HB-SRT method (H-S method). The attenuation correction methods were tested with a simple synthetic DPR dataset. As long as the SRT gives perfect estimates of path-integrated attenuation and the adjustment factor of the k- Z e relationship (denoted by ε) is vertically constant, the H-S method is much better than the dual-frequency methods. Tests with SRT error and vertical variation in ε showed that the H-D method was better than the H-S method for weak precipitation, whereas the H-S method was better than the H-D method for heavy precipitation. The H-D-S method did not produce the best results for both weak and heavy precipitation, but the results are stable. Quantitative evaluation should be done with real DPR measurement datasets. [ABSTRACT FROM AUTHOR]

In high-resolution radar systems, stretch processing technique is generally used to process the returned signals of a range-spread target. Consequently, this results in mixer outputs that consist of several linear frequency modulation components with a small frequency rate (FR). The authors define an FR function to transform a signal from the time domain into the FR domain and discuss the FR range of a discrete signal. Then an interesting phenomenon is found in the FR domain, that is, the mixer output is concentrated in a low FR region, while the Gaussian noise spreads over the entire FR region. Therefore, a range-spread target detector has been proposed that regards the integration of the values in a vicinity of zero FR as the test statistic. This detector uses a single mixer output and thus has the ability to detect the target with a high velocity. Finally, the proposed detector is evaluated by recorded radar data. Experimental results show that the proposed detector outperforms the detectors using the high-resolution range profile. [ABSTRACT FROM AUTHOR]

Highlights: [•] Interferometric radar sensor. [•] Level gauge. [•] Field tests. [•] Laboratory tests. [Copyright &y& Elsevier]

Because of its extensive quality control procedures and uniform space-time grid, the NCEP Stage IV merged Weather Surveillance Radar-1988 Doppler (WSR-88D) radar and surface rain gauge dataset is often considered to be the best long-term gridded dataset of precipitation observations covering the contiguous United States. Stage IV accumulations are employed in a variety of applications, and while the WSR-88D systems are well suited for observing heavy rain events that are likely to affect flooding, limitations in surface radar and gauge measurements can result in missed precipitation, especially near topography and in the western United States. This paper compares hourly Stage IV observations of precipitation occurrence to collocated observations from the 94-GHz CloudSat Cloud Profiling Radar, which provides excellent sensitivity to light and frozen precipitation. Statistics from 4 yr of comparisons show that the CloudSat observes precipitation considerably more frequently than the Stage IV dataset, especially in northern states where frozen precipitation is prevalent in the cold season. The skill of Stage IV for precipitation detection is found to decline rapidly when the near-surface air temperature falls below 0°C. As a result, agreement between Stage IV and CloudSat tends to be best in the southeast, where radar coverage is good and moderate-to-heavy liquid precipitation dominates. Stage IV and CloudSat precipitation detection characteristics are documented for each of the individual river forecast centers that contribute to the Stage IV dataset to provide guidance regarding potential sampling biases that may impact hydrologic applications. [ABSTRACT FROM AUTHOR]

The study aimed to discuss the application of CINRAD/SA radar using negative elevation angle mode in observation of tropical cyclone. Firstly, the calculation formula of the lowest detecting height of CINRAD/SA radar was educed, and then the application of negative angle mode in Changle Radar Station was introduced. Through analyzing different observing abilities for tropical cyclone detected at different elevation angles, we discussed the limitation of CINRAD/SA radar using negative angle mode, and finally proposed some suggestions on CINRAD/SA radar using negative elevation angle mode to observe tropical cyclone. [ABSTRACT FROM AUTHOR]

Widely distributed radar network architectures can provide significant performance improvement for target detection and localization. For a fixed radar network, the achievable target detection performance may go beyond a predetermined threshold with full transmitted power allocation, which is extremely vulnerable in modern electronic warfare. In this paper, we study the problem of low probability of intercept (LPI) design for radar network and propose two novel LPI optimization schemes based on information-theoretic criteria. For a predefined threshold of target detection, Schleher intercept factor is minimized by optimizing transmission power allocation among netted radars in the network. Due to the lack of analytical closed-form expression for receiver operation characteristics (ROC), we employ two information-theoretic criteria, namely, Bhattacharyya distance and J-divergence as the metrics for target detection performance. The resulting nonconvex and nonlinear LPI optimization problems associated with different information-theoretic criteria are cast under a unified framework, and the nonlinear programming based genetic algorithm (NPGA) is used to tackle the optimization problems in the framework. Numerical simulations demonstrate that our proposed LPI strategies are effective in enhancing the LPI performance for radar network. [ABSTRACT FROM AUTHOR]

Most of the existing antenna scan type (AST) identification techniques focus on only mechanical scanning (MS) and rarely research electronic scanning (ES), especially one-dimensional (1D) ES. This paper proposes a recognition and parameter extraction method of 1D ES for 3D radar, which employs both MS and ES. An ES pattern simulator is designed to synthesize pulse amplitude (PA) data first. Then 1D ES is distinguished from MS and two-dimensional (2D) ES based on the features extracted from the difference sequences of beam sequence, such as resemblance coefficient (RC) and variance. Subsequently, two vital parameters for 1D ES are extracted, namely, beam position number in elevation and pulse number in a beam position. Finally, some simulation results are shown to validate the proposed algorithm. [ABSTRACT FROM AUTHOR]

Cross-borehole pulse radar has been employed to detect a deeply located empty tunnel. In this paper, effects of underground water collected in the bottom of an empty tunnel on cross-borehole pulse radar signatures are analyzed numerically. B-scan images, stacks of received pulses, are calculated by applying the finite-difference time-domain (FDTD) method for 6 different heights of water from the bottom to the half height inside an empty tunnel. The most important features of an empty tunnel, the fastest time of peak (TOP) and time of arrival (TOA) extracted from the B-scan images, are slowed considerably depending on the increased height of water inside the tunnel. To compensate the weak TOP like that of an empty tunnel, a relation curve is formulated only utilizing measurable parameters of the fastest TOP and the fastest TOA. Then, a unified curve including the effects of two granites with the low and high dielectric properties is derived to cover widely varied dielectric properties of underground rocks. Based on the fastest TOP of an empty tunnel, the average difference between the fastest TOP of an empty tunnel and that of a partially water-filled tunnel decreases from 22.92% to 2.59% after enhancement. [ABSTRACT FROM AUTHOR]

This paper presents new methods for rainfall estimation from X-band dual-polarization radar observations along with advanced techniques for quality control, hydrometeor classification, and estimation of specific differential phase. Data collected from the Hydrometeorology Testbed (HMT) in orographic terrain of California are used to demonstrate the methodology. The quality control and hydrometeor classification are specifically developed for X-band applications, which use a 'fuzzy logic' technique constructed from the magnitude of the copolar correlation coefficient and the texture of differential propagation phase. In addition, an improved specific differential phase retrieval and rainfall estimation method are also applied. The specific differential phase estimation is done for both the melting region and rain region, where it uses a conventional filtering method for the melting region and a self-consistency-based method that distributes the total differential phase consistent with the reflectivity factor for the rain region. Based on the specific differential phase, rainfall estimations were computed using data obtained from the NOAA polarimetric X-band radar for hydrometeorology (HYDROX) and evaluated using HMT rain gauge observations. The results show that the methodology works well at capturing the high-frequency rainfall variations for the events analyzed herein and can be useful for mountainous terrain applications. [ABSTRACT FROM AUTHOR]

Radar refractivity retrievals can capture near-surface humidity changes, but noisy phase changes of the ground clutter returns limit the accuracy for both klystron- and magnetron-based systems. Observations with a C-band (5.6 cm) magnetron weather radar indicate that the correction for phase changes introduced by local oscillator frequency changes leads to refractivity errors no larger than 0.25 N units: equivalent to a relative humidity change of only 0.25% at 20°C. Requested stable local oscillator (STALO) frequency changes were accurate to 0.002 ppm based on laboratory measurements. More serious are the random phase change errors introduced when targets are not at the range-gate center and there are changes in the transmitter frequency (Δ fTx) or the refractivity (Δ N). Observations at C band with a 2-μs pulse show an additional 66° of phase change noise for a Δ fTx of 190 kHz (34 ppm); this allows the effect due to Δ N to be predicted. Even at S band with klystron transmitters, significant phase change noise should occur when a large Δ N develops relative to the reference period [e.g., ~55° when Δ N = 60 for the Next Generation Weather Radar (NEXRAD) radars]. At shorter wavelengths (e.g., C and X band) and with magnetron transmitters in particular, refractivity retrievals relative to an earlier reference period are even more difficult, and operational retrievals may be restricted to changes over shorter (e.g., hourly) periods of time. Target location errors can be reduced by using a shorter pulse or identified by a new technique making alternate measurements at two closely spaced frequencies, which could even be achieved with a dual-pulse repetition frequency (PRF) operation of a magnetron transmitter. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of a hail event that occurred 27 May 2012 over Brignoles, located in southeastern France. The event was observed by an X-band polarimetric radar located in Mont Maurel, 75 km northeast of the hailstorm. Lightning data from the French national network (owned and operated by Météorage) are also used in the study. The analysis highlights that the lightning and radar data provide complementary information that may allow a better microphysical interpretation of the hailstorm and potentially increase the probability of its detection. [ABSTRACT FROM AUTHOR]

HF radar data are often collected for time periods that are optimized for current measurement applications where, in many cases, very high temporal resolution is needed. Previous work has demonstrated that this does not provide sufficient averaging for robust wave measurements to be made. It was shown that improvements could be made by averaging the radar data for longer time periods. HF radar provides measurements over space as well as in time, so there is also the possibility to average in space. However, the radar data are correlated in space because of the range and azimuth processing. The implications of this are discussed and estimates of the impact on the reduction in variance in the radar Doppler spectral estimates are obtained. Spatial inhomogeneities and temporal nonstationarity in the ocean wave field itself also need to be taken into account. It is suggested that temporal averaging over periods of up to one hour and spatial averaging over 9-25 nearest neighbors may be suitable, and these will be explored in later work. [ABSTRACT FROM AUTHOR]

Sea surface height (SSH) measurements provided by pulse-limited radar altimeters are one-dimensional profiles along the satellite's nadir track, with no information whatsoever in the cross-track direction. The anisotropy of resulting SSH profiles is the most limiting factor of mesoscale SSH maps that merge the 1D profiles. This paper explores the potential of the cross-track slope derived from the Cryosphere Satellite-2 ( CryoSat-2)'s synthetic aperture radar interferometry (SARin) mode to increase the resolution of mesoscale fields in the cross-track direction. Through idealized 1D simulations, this study shows that it is possible to exploit the dual SARin measurement (cross-track slope and SSH profile) in order to constrain mesoscale mapping in the cross-track direction. An error-free SSH slope allows a single SARin instrument to recover almost as much SSH variance as two coordinated altimeters. Noise-corrupted slopes can also be exploited to improve the mapping, and a breakthrough is observed for SARin errors ranging from 1 to 5 μrad for 150-km-radius features in strong currents, and 0.1-0.5 μrad for global mesoscale. Although only limited experiments might be possible with the error level of current CryoSat-2 data, this paper shows the potential of the SAR interferometry technology to reduce the anisotropy of altimeter measurements if the SARin error is significantly reduced in the future, and in particular in the context of a prospective SARin demonstrator optimized for oceanography. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) for urban operations and through-the-wall radar imaging has been shown to be successful in fast data acquisition and moving target localizations. The research in this area thus far has assumed effective removal of wall electromagnetic backscatterings prior to CS application. Wall clutter mitigation can be achieved using full data volume which is, however, in contradiction with the underlying premise of CS. In this paper, we enable joint wall clutter mitigation and CS application using a reduced set of spatial-frequency observations in stepped frequency radar platforms. Specifically, we demonstrate that wall mitigation techniques, such as spatial filtering and subspace projection, can proceed using fewer measurements. We consider both cases of having the same reduced set of frequencies at each of the available antenna locations and also when different frequency measurements are employed at different antenna locations. The latter casts a more challenging problem, as it is not amenable to wall removal using direct implementation of filtering or projection techniques. In this case, we apply CS at each antenna individually to recover the corresponding range profile and estimate the scene response at all frequencies. In applying CS, we use prior knowledge of the wall standoff distance to speed up the convergence of the orthogonal matching pursuit for sparse data reconstruction. Real data are used for validation of the proposed approach. [ABSTRACT FROM PUBLISHER]

We consider sparsity-driven change detection (CD) for human motion indication in through-the-wall radar imaging and urban sensing applications. Stationary targets and clutter are removed via CD, which converts a populated scene into a sparse scene of a few human targets moving inside enclosed structures and behind walls. We establish appropriate CD models for various possible human motions, ranging from translational motions to sudden short movements of the limbs, head, and/or torso. These models permit scene reconstruction within the compressive sensing framework. Results based on laboratory experiments show that a sizable reduction in the data volume is achieved using the proposed approach without a degradation in system performance. [ABSTRACT FROM PUBLISHER]

Polarimetric synthetic aperture radar (PolSAR) image classification, an important technique in the remote sensing area, has been deeply studied for a couple of decades. In order to develop a robust automatic or semiautomatic classification system for PolSAR images, two important problems should be addressed: 1) incorporation of spatial relations between pixels; 2) estimation of the number of classes in the image. Therefore, in this paper, we present a novel superpixel-based classification framework with an adaptive number of classes for PolSAR images. The approach is mainly composed of three operations. First, the PolSAR image is partitioned into superpixels, which are local, coherent regions and preserve most of the characteristics necessary for image information extraction. Then, the number of classes and each class center within the data are estimated using the pairwise dissimilarity information between superpixels, followed by the final classification operation. The proposed framework takes the spatial relations between pixels into consideration and makes good use of the inherent statistical characteristics and contour information of PolSAR data. The framework is capable of improving the classification accuracy, making the results more understandable and easier for further analyses, and providing robust performance under various numbers of classes. The performance of the proposed classification framework on one synthetic and three real data sets is presented and analyzed; and the experimental results show that the framework provides a promising solution for unsupervised classification of PolSAR images. [ABSTRACT FROM PUBLISHER]

Antenna array synthesis is an important issue in MIMO radars. By judiciously designing antenna positions, one can create a very long virtual array steering vector with a small number of antennas and therefore achieve very high spatial resolution at a small cost. This paper presents a combinatorial methodology based on cyclic difference sets (CDSs) for minimum redundancy (MR) MIMO array synthesis which seeks to maximize the virtual array aperture for a given number of antennas. First, the key features of CDSs and the CDS-based MR-MIMO layouts are described. Then, the analytical expression of the maximum contiguous virtual array aperture is derived. Further, based on this expression, an enumerative shifting procedure is developed for identifying the optimal CDS-based MR-MIMO layout. Selected examples are analyzed to point out the computational effectiveness of the CDS-based MRMIMO array synthesis. [ABSTRACT FROM AUTHOR]