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This paper introduces a method based on a deep neural network (DNN) that is perfectly capable of processing radar data from extremely thinned radar apertures. The proposed DNN processing can provide both aliasing-free radar imaging and super-resolution. The results are validated by measuring the detection performance on realistic simulation data and by evaluating the Point-Spread-function (PSF) and the target-separation performance on measured point-like targets. Also, a qualitative evaluation of a typical automotive scene is conducted. It is shown that this approach can outperform state-of-the-art subspace algorithms and also other existing machine learning solutions. The presented results suggest that machine learning approaches trained with sufficiently sophisticated virtual input data are a very promising alternative to compressed sensing and subspace approaches in radar signal processing. The key to this performance is that the DNN is trained using realistic simulation data that perfectly mimic a given sparse antenna radar array hardware as the input. As ground truth, ultra-high resolution data from an enhanced virtual radar are simulated. Contrary to other work, the DNN utilizes the complete radar cube and not only the antenna channel information at certain range-Doppler detections. After training, the proposed DNN is capable of sidelobe- and ambiguity-free imaging. It simultaneously delivers nearly the same resolution and image quality as would be achieved with a fully occupied array.

Sensor fusion is a critical aspect in autonomous drone navigation as several tasks, such as object detection and self-pose estimation, require combining information from heterogeneous sources. The performance of these solutions depends on several factors, such as the characteristics of the sensors and the environment, as well as the computing platforms, which can heavily impact their accuracy and response time. Carrying out such performance evaluations through real flight tests can be a resource-demanding, time-consuming, and, at times, risky process, which is why researchers often rely on simulation environments for testing and validating sensor fusion algorithms. The simulation environment should provide photorealistic environmental features, as well as a comprehensive set of sensors, in order to allow to test the most extensive set of sensor fusion algorithms. This paper presents AirLoop, an AirSim-based flight simulator for Hardware-in-the-Loop and Software-in-the-Loop algorithm testing and validation. AirLoop extends the sensor setup provided by AirSim with an FMCW RADAR sensor simulation, which has been evaluated based on the Infineon Technologies BGT60TR13C RADAR. Furthermore, this work provides several Software-in-the-Loop (SITL) and Hardware-in-the-Loop (HITL) demonstrations, including interfacing with the Pixhawk 2 flight controller and an extensive evaluation of the communication of the engine with the NVIDIA Jetson Nano, which has been evaluated in various use cases, including dataset creation, object detection, Path Planning, and Simultaneous Localization and Mapping (SLAM).

This study compares the actual experimental and simulation data of artillery projectiles (M107 projectiles) fired by a 155-mm gun. The experimental data are collected using a weapon-locating radar developed by LigNex1. The simulation is performed based on the measured projectile trajectories. Additionally, full-polarimetric scattering of the target is calculated using the full-wave method. The time series and statistics of the measured and simulated radar cross-section data are compared, and the differences between the two are addressed.

The SHAllow RADar (SHARAD) onboard Mars Reconnaissance Orbiter (MRO) is a 20 MHz Synthetic Aperture Radar (SAR) that probes the first hundreds of meters of the Martian subsurface. In order to interpret the detection of subsurface interfaces with ground penetrating radars, simulations using Digital Terrain Models (DTM) are necessary. This methodology paper focuses on the analysis of the first tens of meters of the Martian subsurface with SHARAD, comparing the use of different high-resolution DTMs for radar simulation, namely, from the High-Resolution Stereo Camera (HRSC) onboard the Mars Express and from the Context Camera (CTX) onboard MRO. The region of Terra Cimmeria was chosen as a demonstration area. It is a highly cratered southern midlatitude region, where, as will be discussed, the higher resolution of the aforementioned terrain models is mandatory to describe the surface at an acceptable level of detail for shallow subsurface radar interpretation. With a DTM corrected by photoclinometry using CTX imagery, we show that a reflector that was visible on SHARAD data but not on the simulation made with an HRSC DTM is, in fact, a surface echo that was not reproduced by the HRSC surface model. We also show that, unlike laser altimetry DTMs, optical DTMs are prone to artifacts that can make radar analysis more complicated for some scenarios. Reciprocally, we show that the comparison between radar and its corresponding simulated data is a way of assessing a DTM's quality, which is especially useful in missions where ground control points are lacking, unlike Martian observations. [ABSTRACT FROM AUTHOR]

This paper presents an overview and novel method on inter-radar interference analysis based on propagation models and radar beam-scanning scenarios. Many studies on radar interference have provided proper methodologies and recommendations for system safety, coexistence, and higher spectrum utilization. However, the propagation models and radar beam-scanning scenarios have not given detail consideration because they usually focus on the proposed interference mitigation techniques. This paper provides a statistical interference analysis method to understand selection of propagation model and various beam-scanning scenarios on the radar interference. The time, duration, probability of the worst-case interference and expected performance degradation is presented statistically. From the simulation result, the maximum gain alignment under 1 degree between two radar occurs with $1.82\times 10^{-5}$ probability in an example scenario. The proposed simulation and analysis method can help to derive radar operational parameters for interference mitigation and efficient spectrum utilization.

This paper introduces a novel deep learning based concept for image enhancement and distortion suppression in automotive radar signal processing. The deep neural network (DNN) is trained solely on virtual data that is generated by an automotive MIMO radar ray tracing simulator. The simulator mimics the raw data that would be provided by a specific automotive MIMO radar for which the signal processing is envisaged. This virtual radar sensor, which has the same properties as the real radar, creates the DNN training input data. The unique feature of our approach is that an advanced virtual radar sensor is used to create the target values for the DNN training. The advanced virtual sensor is a simulated radar that works on the same basic principle as the real radar and its resembling virtual sensor, but is significantly upgraded in terms of performance. In our experiments the advanced virtual sensor, e.g., has a notably larger array, i.e. a much better lateral resolution than the real radar. In addition, our advanced virtual sensor is not affected by multi-path effects, clutter, noise, beamforming sidelobes and other typical automotive radar distortions. The paper shows that a DNN trained on this advanced virtual sensor training data can deliver outstanding automotive radar signal processing results not only on simulated data but also on field data. The presented real-world automotive radar measurements show that multi-path, clutter and noise are efficiently suppressed by this DNN and that it has a remarkable ability to sharpen images and suppress sidelobes. The results presented in the paper suggest that the novel advanced DNN training concept based on virtual sensors offers opportunities that also go far beyond the radar signal processing capabilities illustrated. Hence, the concept put forward here is an attractive option for placing DNN-based radar signal processing on a novel and easy-to-implement foundation.

With the rise of high resolution multiple input multiple output (MIMO) systems, radar became an important sensor in the development of Advanced Driver Assistance Systems (ADAS) and autonomous driving applications. Autonomous driving will rely strongly on artificial intelligence. Since most modern classification algorithms are based on neural networks, they require huge amounts of data to perform well, especially in unexpected traffic situations. Radar sensor simulation can potentially produce a great variety of training data for machine learning algorithms, which makes it an important cornerstone in the development of ADAS. Furthermore, with radar simulators, different antenna configurations and various edge cases can be simulated. In this work, a versatile ray tracing toolchain based on the shoot and bouncing rays (SBR) approach is presented. The program is able to simulate complex urban environments including realistic clutter, by utilizing simplistic reflection models. The program does not only produce realistic radar images, but also generates camera-like images using the same materials. Furthermore, this work deals with the adaption of the SBR method to radar sensors with an arbitrary number of transmit (TX)- and receive (RX) antennas, which enables the simulation of large MIMO arrays. A novel performance optimization approach is proposed for large numbers of TX antennas, which reduces the runtime dramatically. The quality of the simulation is verified by measuring a complex and realistic scenario with a high resolution automotive MIMO radar. Also, a study of the effect on quality and runtime is being investigated for various optimization approaches, including the proposed method.

The SHAllow RADar (SHARAD) onboard Mars Reconnaissance Orbiter (MRO) is a 20 MHz Synthetic Aperture Radar (SAR) that probes the first hundreds of meters of the Martian subsurface. In order to interpret the detection of subsurface interfaces with ground penetrating radars, simulations using Digital Terrain Models (DTM) are necessary. This methodology paper focuses on the analysis of the first tens of meters of the Martian subsurface with SHARAD, comparing the use of different high-resolution DTMs for radar simulation, namely, from the High-Resolution Stereo Camera (HRSC) onboard the Mars Express and from the Context Camera (CTX) onboard MRO. The region of Terra Cimmeria was chosen as a demonstration area. It is a highly cratered southern midlatitude region, where, as will be discussed, the higher resolution of the aforementioned terrain models is mandatory to describe the surface at an acceptable level of detail for shallow subsurface radar interpretation. With a DTM corrected by photoclinometry using CTX imagery, we show that a reflector that was visible on SHARAD data but not on the simulation made with an HRSC DTM is, in fact, a surface echo that was not reproduced by the HRSC surface model. We also show that, unlike laser altimetry DTMs, optical DTMs are prone to artifacts that can make radar analysis more complicated for some scenarios. Reciprocally, we show that the comparison between radar and its corresponding simulated data is a way of assessing a DTM’s quality, which is especially useful in missions where ground control points are lacking, unlike Martian observations.

Radar sounders (RSs) are low-frequency instruments that profile the shallow subsurface of planetary targets to obtain valuable scientific information. The prediction of the RS performance and the interpretation of the target properties from the RS data are challenging due to the complex electromagnetic interaction among many acquisition variables. Simulation of RS data can address this issue by modeling the complex interaction and producing simulated radargrams representing the acquisition scenario. In this article, we present an approach to generate databases of geoelectrical models and simulated radargrams corresponding to combinations of: 1) target geoelectrical hypotheses; 2) RS parameters; and 3) acquisition geometry configurations. The proposed approach exploits this database for: 1) predicting the detection performance and sensitivity of the RS and 2) understanding the interpretability of the underlying hypotheses. In order to identify hypothesis combinations that can be unambiguously inverted from the radargrams, we analyze the similarity between pairs of geoelectrical models and between the simulated radargrams, and the statistical distance between radargram features. The approach is demonstrated for the case of Radar for Icy Moons Exploration (RIME), using three selected targets on the Jovian moon Ganymede, with three different simulation techniques. The results are very promising and reveal the effectiveness of the proposed approach in extracting valuable information regarding: 1) the target detection performance of RIME; 2) the sensitivity to the dielectric contrast; 3) the separability of radargram features; and 4) the identification of hypothesis combinations producing significantly different radar response, and thus invertible. [ABSTRACT FROM AUTHOR]

Based on a certain aero-engine simplified exhaust cavity radar cross section（RCS）test results, the contribution of the stabilizer taking the reduction measures to the engine’s rearward cavity RCS is analyzed. Based on the traditional structure of the afterburner, the simplified cavity RCS calculation models of five different stabilizer evaporation chambers are established for the existing structure. The RCS characteristics of different stabilizer evaporation chamber structures are compared at high frequency 10 GHz and low frequency 1.5 GHz. The results show that, in the traditional non-stealth integrated design of the exhaust cavity structure, the stabilizer can achieve an obvious reduction effect of RCS at high frequency after adopting stealth measures. Several kinds of modified design for the structure of evaporation chamber of the flame stabilizer can bring a certain reduction benefit at high frequency, and the reduction at low frequency is not obvious. The flat structure with transverse and longitudinal composite angle inclination（WDQ5） has better improvment on RCS reduction than other structure, and can improve the working condition of absorbing coating and its reliability. Stabilization of the stabilizer at 0°—20° has a certain inhibitory effect on the RCS of the backward cavity. [ABSTRACT FROM AUTHOR]

Multifunction phased array radars have the ability to steer the radar beam electronically, adapting its parameters according to how the radar perceives the environment. Thus, the effective allocation of the available resources is very important. A model of a multifunction phased array radar was implemented to examine the complexities involving this type of radar. Additionally, approaches to adaptively assigning priorities of targets and sectors of surveillance are developed and assessed. Ranking radar tasks is an important sub-problem for radar resource management. When the radar resources are not sufficient to undertake all its functions, their priorities will indicate an appropriate manner of reallocating the radar resources. In this paper, simulation methods to evaluate prioritization of tasks are described. [ABSTRACT FROM AUTHOR]

The Agile Dual Auroral Radar Network (AgileDARN) radar has been developed as an all‐digital high‐frequency (HF) coherent scatter radar. Based on phased array antenna and digital signal processing techniques, digital beam forming (DBF) is employed for both transmission and reception, which steers toward the desired direction based on the relative phases within the array elements. However, the amplitude and phase imbalances among channels will lead to substantial degradation of DBF performance, including the beam direction deviation, broadening of the main lobe, deteriorated side lobes, antenna gain degradation, and ultimately affects the radar detection accuracy. Here, we present a complete method for calibrating these imbalances in transceivers and linear array elements in AgileDARN radar: internal calibration and external calibration. During the internal calibration, the calibration signals with the same amplitude and phase as the multi‐pulse sequence are sent to all transceivers internally. The variance of the outputs illustrates the transceivers' imbalances and can be processed to implement calibration. Further, the meteor echoes and linear least squares fitting method are employed for external calibration, which calibrates the amplitude and phase imbalances among the cables and phased array elements. The technique has been successfully implemented, and we present the results of measured data observed by the AgileDARN HF radar before and after calibration. Key Points: Developed a calibration mode and proposed a complete system calibration method for Agile Dual Auroral Radar Network radarInject two signals to eliminate the errors among transceivers, enabling the amplitude and phase mismatches to reduce to 0.2 dB and 1°Employ the linear least squares fitting with meteor echoes for antenna calibration, facilitating the array pattern close to an ideal one [ABSTRACT FROM AUTHOR]

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In the modern rapidly changing battlefield, the resources of multiple radars are usually limited for multiple objects multiple missions. Moreover, the amount of target information acquired by different radars at different time index is different. Thus, as to target tracking, it is important to reasonably and dynamically allocate radars' resources. In the study, based on information theory, the entropy is utilised to quantitatively measure the amount of target information observed from multiple radars. Corresponding, the lower bound (LB) of the given entropy is also derived. The smaller the value of the entropy, the more accurate the estimate of target motion state is. So based on minimising the entropy LB of target information acquired from radars at different times, a new fusion tracking method is proposed to dynamically adaptively choose radars with high amount of target information for target tracking. The simulation results show that the proposed method has higher tracking accuracy than the fusion tracking without optimal choice of radars. [ABSTRACT FROM AUTHOR]

In order to realise effective target detection with a large baseline radar network interfered by a single radiation source, an adaptive processing method derived from traditional principal component analysis (PCA) and array signal processing theory is proposed in this study. First, the signal model for target detection with a large baseline radar system in an interference environment is established. Second, based on the characteristics of the signal model, an adaptive suppression method composed of high-precision delay alignment and modified PCA is proposed. Finally, the effectiveness of the method in a typical environment is verified by simulation. The results show that this method can accumulate target energy effectively and realise reliable detection in such case, which has great application prospects. [ABSTRACT FROM AUTHOR]

A kind of high speed and parallel hardware architecture is proposed and designed for digital signal processing of high frequency Pulse Doppler radar here. The platform is based on one XC7K410T FPGA, two XC7K325T FPGAs, one TMS320C6678 DSP, and four sets of MT41J256M8 DDR3. The details of implementation including pulse compression, moving target detection and constant-false-alarm rate are described. The simulation results and resource consumption are presented to demonstrate the advantages of the proposed FPGA implementation architecture. [ABSTRACT FROM AUTHOR]

This article addresses a high-resolution wide swath (HRWS) Terrain Observation by Progressive Scans (TOPSAR) mode which integrates the azimuth multichannel HRWS imaging technique and TOPSAR technique. More equivalent phase centres are obtained through the azimuth single-input and multiple-output technique, which would provide more azimuth samples. Hence, a more reliable and accurate imaging results of the large width terrain profile would be obtained. The performance of this HRWS TOPSAR mode is evaluated theoretically. A new processing method for this mode is provided and the results of simulation data processing validate the analysed results and the new method. [ABSTRACT FROM AUTHOR]

Due to the high cost of real experiments, radar data simulation plays an important role in radar applications. However, the accuracy and the calculation speed of existing simulation methods is limited by the model error and the heavy calculation of electromagnetic simulation. Here, a radar data simulation method based on deep generative network (DGN) is proposed. DGN is generative model involving deep network as the representation tool, in which the model is trained with labelled data. When the training phase is finished, the generative model can generate data samples which are similar to the real samples. The performance of the DGN is evaluated on the ground-based radar dataset, and the results show that the distribution of the generated radar data is similar to the training radar data. [ABSTRACT FROM AUTHOR]

The performance of long range radar is affected by the atmospheric conditions and terrain, as well as the beam operation of the radar system. In this paper, a modeling and simulation(M&S) tool was developed considering the external environment and radar systems of long range radars for performance prediction, and was described using the verification method. Atmospheric refraction and wave propagation were simulated using APM, to analyze the effects of the external environment. The beam template of radar, which is used for actual radar systems, was utilized to analyze the effects of beam operation. The developed M&S tool was verified by comparison with the experimental results obtained from three different situations and confirmed to be highly effective in predicting the performance of long range radar. [ABSTRACT FROM AUTHOR]

In this work, a novel approach for the simulation of synthetic aperture radar (SAR) images is proposed. Also, this approach aims to interpret the mechanisms of the dominated scattering centres in the SAR image. An attributed model based on typical structures is used to characterise the 3D geometry of the targets. The physical optics and the geometry optics are used to calculate the main scattering mechanisms of the specified typical structures. Also, based on scattering properties and the imaging process, the simulation approach is set up. The experiments based on the Terra-SAR data of an airplane are implemented to test the validity of the simulation. The work presented would be useful for SAR automatic target recognition. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the problem of radar signal waveform design under the small signal power conditions for detecting a doubly spread target in colored noise. The doubly spread target is range spread and its impulse response is time varying due to fluctuation (hence Doppler spread). The locally most powerful detector is derived for detecting such targets. The signal waveform is optimized to maximizing the detector's detection performance or equivalently the Kullback–Leibler divergence. Numerical simulations validate the effectiveness of the proposed waveform design by comparing it with the frequency modulated (LFM) waveform. [ABSTRACT FROM AUTHOR]

We present a new synthetic aperture radar (SAR) raw signal simulator, which is able to simultaneously generate raw signals of different polarimetric channels of a polarimetric SAR system in such a way that a correct covariance matrix is obtained for the final images. Extended natural scenes, dominated by surface scattering, are considered. A fast Fourier-domain approach is used for the generation of raw signals. Presentation of theory is supplemented by meaningful experimental results, including a comparison of simulations with real polarimetric scattering data. [ABSTRACT FROM AUTHOR]

This study presents a novel method for low angle estimation in diffuse multipath environment by the time-reversal (TR) minimum-norm-like (MNL) technique. In recent studies, the diffuse multipath is always regarded as the interference which is tried to be eliminated or weakened in low angle estimation for very high-frequency radar. However, it is difficult to establish the accurate model of the multipath environment. The common methods usually ignore the diffuse multipath and use the mirror reflection as the approximate model, but the ideal assumption would cause great errors. The proposed method makes the radar array working in the TR setup and the MNL technique is applied to estimate the low angle. In the proposed method, the exact model of the multipath environment does not need to be established completely but the accuracy performance of low angle estimation can be improved by making use of the multipath. Simulation results are presented to demonstrate the effectiveness of the proposed method. In addition, the performance degradation with respect to target motion is also shown in simulation results. [ABSTRACT FROM AUTHOR]

This paper presents methods and results in modeling wind turbine dynamic radar signatures in the near field. The theoretical analysis begins with the simpler case of modeling wind turbine blades as rectangular plates. The theoretical radar signature for the wind turbine in the near field is formulated and its main peculiarities are investigated. Subsequently, the complex shape of the blades is considered and the corresponding radar signatures are modeled. Theoretical modeling is confirmed for both cases via experimental testing in laboratory conditions. It is shown that the experimental results are in good accordance with the theoretically predicted signatures. [ABSTRACT FROM AUTHOR]

The estimation of delay and Doppler shift parameters in the presence of multiple targets in radar applications are addressed in this study. The construction of a new ambiguity function, namely, exponential ambiguity function, is proposed by introducing an exponent $p$ into the traditional ambiguity function. Some important properties of the exponential ambiguity function are derived. Then the joint delay and Doppler shift estimation algorithm is proposed based on the combination of the exponential ambiguity function and wavelet denoising method. The performance of the proposed estimation algorithm is theoretically analyzed, verified via Monte Carlo simulation, and compared with the performance of the traditional ambiguity function. Simulation results and theoretical performance analysis show that the proposed estimation algorithm outperforms the existing methods for closely located targets. [ABSTRACT FROM PUBLISHER]

This paper proposes an approach to position the drifting aircraft debris after a crash accident based on a hybrid model, which includes a mechanical calculus part and an advanced ocean model part. The most probable crash area is firstly established via mechanical calculus analysis on the normal forced landing process and the abnormal crashing process. Then the numerical simulation is taken to calculate the drifting process of the debris. A simulation approach based on the grid-measurement and the improved ocean model is used to predict the most possible domain of the drifting debris in the several following days. The impact of using the advanced ocean model on positioning the drifting debris is studied, on which the idealized current flow is used but the effect of winds is ignored. The case study of MH370 and the sensitivity analysis indicate the validity of the model. The proposed work enables further research on quick positioning of drifting debris after an aircraft crash. [ABSTRACT FROM AUTHOR]

To obtain accurate radar-measured wind measurements in tornadoes, differences between air and Doppler velocities must be corrected. These differences can cause large errors in radar estimates of maximum tangential wind speeds, and large errors in single-Doppler retrievals of radial and vertical velocities. Since larger scatterers (e.g., debris) exhibit larger differences from air velocities compared to small scatterers (e.g., raindrops), the dominant scatterer type affecting radar measurements is examined. In this study, radar variables are simulated for common weather radar frequencies using debris and raindrop trajectories computed with a large-eddy simulation model and two electromagnetic scattering models. These simulations include a large range of raindrop and wood board sizes and concentrations, and reveal the significant frequency dependence of the equivalent reflectivity factor and Doppler velocity. At S band, dominant scatterers are wood boards, except when wood board concentrations are very low. In contrast, raindrops are the dominant scatterers at Ka and W bands even when large concentrations of wood boards are present, except for low raindrop concentrations. Dual-wavelength velocity differences exhibit high correlation with air and Doppler velocity differences for most cases, which may enable direct measurements of scatterer-induced Doppler velocity bias in tornadoes. Moreover, dual-wavelength ratios are shown to exhibit strong correlations with dominant scatterer size, except when Rayleigh scatterers are dominant. Finally, vertical velocity retrievals are shown to exhibit lower errors at high frequencies, and large errors remain at centimeter wavelengths even after debris centrifuging corrections are applied in cases with high debris concentration. [ABSTRACT FROM AUTHOR]

In maritime simulators there are problems of discontinuity and saltation on radar simulation images caused by the limited range of radar simulation data generated from a single electronic chart. Furthermore, this kind of radar simulation is overlapping, discontinuous and redundant, and thus could lead to simulation error and training interruption. To address this problem, a method is presented to generate a wide range of radar simulation data based on a multi-scale chart data. The method uses automatic splicing on same-scale maps and variable-scale visualisation splicing on multi-scale maps. In this paper, the S-57 standard electronic navigational charts are filtered from the S-57 database and then radar simulation data are generated based on charts of different scales. From clipping, sorting, deletion, combination and matching, a wide range of continuous radar simulation data are generated. Taking the waters around Ningbo Port, China as an example, 27 chart sections are filtered out from S-57 database, with a longitude range from 121°33' to 122°56.8' and a latitude range from 29°3' to 30°21.8'. The wide-range and continuous radar simulation data are generated for a maritime simulator. The results validate this method as being highly accurate and able to meet the practical needs of maritime simulation. [ABSTRACT FROM PUBLISHER]

In recent years, adaptive (data dependent) methods have been introduced into many areas where Fourier spectral analysis has traditionally been used. Although the data-dependent methods are often advanced as being superior to Fourier methods, they do require some finesse in choosing the order of the relevant filters. In performing comparisons, we have found some concerns about the mappings, particularly when related to cases involving many spectral lines or even continuous spectral signals. Using numerical simulations, several comparisons between Fourier transform procedures and minimum variance method (MVM) have been performed. For multiple frequency signals, the MVM resolves most of the frequency content only for filters that have more degrees of freedom than the number of distinct spectral lines in the signal. In the case of Gaussian spectral approximation, MVM will always underestimate the width, and can misappropriate the location of spectral line in some circumstances. Large filters can be used to improve results with multiple frequency signals, but are computationally inefficient. Significant biases can occur when using MVM to study spectral information or echo power from the atmosphere. Artifacts and artificial narrowing of turbulent layers is one such impact. [ABSTRACT FROM AUTHOR]

This paper presents a data reduction algorithm to increase the efficiency of radar simulation. The features of artificial and natural shoreline data are analyzed on the basis of original shoreline data, and the characteristic points of shoreline data are extracted on the whole by identifying the direction and curvature of the vector shorelines and using the topology controlling algorithm. Characteristic points of shoreline data are also extracted locally using an optimization algorithm of area loss ratio control. Therefore, all data points that represent shoreline features are extracted as the reduction result. Theoretical analysis of the results indicates that the proposed algorithm has a high data reduction rate. Moreover, the reduced data retains the shoreline's geometric features and fits well with the shoreline. Further, the accuracy analysis results show that the geometric accuracy of the algorithm is sufficiently precise for radar simulation. [ABSTRACT FROM AUTHOR]

Accurate calibration of radar reflectivity is integral to quantitative radar measurements of precipitation and a myriad of other radar-based applications. A statistical method was developed that utilizes the probability distribution of clutter area reflectivity near a stationary, ground-based radar to provide near-real-time estimates of the relative calibration of reflectivity data. The relative calibration adjustment (RCA) method provides a valuable, automated near-real-time tool for maintaining consistently calibrated radar data with relative calibration uncertainty of ±0.5 dB or better. The original application was to S-band data in a tropical oceanic location, where the stability of the method was thought to be related to the relatively mild ground clutter and limited anomalous propagation (AP). This study demonstrates, however, that the RCA technique is transferable to other S-band radars at locations with more intense ground clutter and AP. This is done using data from NASA's polarimetric (NPOL) surveillance radar data during the Iowa Flood Studies (IFloodS) Global Precipitation Measurement (GPM) field campaign during spring of 2013 and other deployments. Results indicate the RCA technique is well capable of monitoring the reflectivity calibration of NPOL, given proper generation of an areal clutter map. The main goal of this study is to generalize the RCA methodology for possible extension to other ground-based S-band surveillance radars and to show how it can be used both to monitor the reflectivity calibration and to correct previous data once an absolute calibration baseline is established. [ABSTRACT FROM AUTHOR]

ABSTRACT We introduce a novel component-level simulation method with which to characterize the performance of the electronic interfaces and circuitry of a magnetic resonance imaging (MRI) receiver. The Matlab-based toolbox first reconstructs idealized coil free induction decay signals from real MR images, then uses these signals to drive a transistor-level and extracted-layout simulation of the desired receiver architecture. The simulated receiver's outputs are read back into the toolbox, which then reconstructs the MR images. By comparing the reconstructed images with the original ones, important design characteristics, such as the circuit's noise figure, linearity, phase noise, and inter-channel coupling, can be investigated in terms of image quality. To validate the method, a new MRI receiver architecture is designed and simulated. The architecture is a 16-channel multiplexer, designed for a commercially available 0.35 μm CMOS technology, and employs both frequency-division and time-division multiplexing to form a combined output signal. The SNR degradation and the circuit's linearity determined from the MR images compare well with the SNR and linearity point predictions from conventional circuit simulations. © 2015 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 44B: 102-113, 2015 [ABSTRACT FROM AUTHOR]

The properties of a unit-fraction section of a spherical Luneburg lens, with two backing ground planes intersecting at the spherical lens's axis, have been evaluated and compared to experimental results. Compared to a full Luneburg lens, the advantage of using a fraction of a Luneburg lens is the reduced weight of the antenna. This reduction is achieved at the expense of the scanning range of the antenna. An analytical expression relating the size of the ground plane to the scanning range is derived. The measured radiation patterns, gain, and scan loss of a hemispherical and of a quarter-sphere lens antenna are presented. The effects of the ground planes on the S parameters of the feed antenna are discussed, and simulated results are compared with measured results. [ABSTRACT FROM AUTHOR]

Radar echo of ballistic midcourse target contains unique motion information of the target, which can provide important evidence for target recognition. A wideband radar echo simulation model for midcourse processional target is developed, where the micro-motion model, electromagnetic scattering calculation and linear frequency modulated (LFM) radar signal model are integrated. Firstly, the position variation of each scattering center of the moving target is analyzed. Then, the high frequency method is used to judge the masking effect of scattering centers of the rotational symmetry target. Finally, the wideband radar echo is simulated, and the impacts of high speed translational motion, non-precession movement and non-idealization of the scattering centers on the echo are also analyzed. [ABSTRACT FROM AUTHOR]

Purpose: To avoid motion artifacts in medical imaging or to minimize the exposure of healthy tissues in radiation therapy, medical devices are often synchronized with the patient's respiratory motion. Today's respiratory motion monitors require additional effort to prepare the patients, e.g., mounting a motion belt or placing an optical reflector on the patient's breast. Furthermore, they are not able to measure internal organ motion without implanting markers. An interesting alternative to assess the patient's organ motion is continuous wave radar. The aim of this work is to design, implement, and evaluate such a radar system focusing on application in CT. Methods: The authors designed a radar system operating in the 860 MHz band to monitor the patient motion. In the intended application of the radar system, the antennas are located close to the patient's body inside the table of a CT system. One receive and four transmitting antennas are used to avoid the requirement of exact patient positioning. The radar waves propagate into the patient's body and are reflected at tissue boundaries, for example at the borderline between muscle and adipose tissue, or at the boundaries of organs. At present, the authors focus on the detection of respiratory motion. The radar system consists of the hardware mentioned above as well as of dedicated signal processing software to extract the desired information from the radar signal. The system was evaluated using simulations and measurements. To simulate the radar system, a simulation model based on radar and wave field equations was designed and 4D respiratory-gated CT data sets were used as input. The simulated radar signals and the measured data were processed in the same way. The radar system hardware and the signal processing algorithms were tested with data from ten volunteers. As a reference, the respiratory motion signal was recorded using a breast belt simultaneously with the radar measurements. Results: Concerning the measurements of the test persons, there is a very good correlation (ρ = 0.917) between the respiratory motion phases received by the radar system and the external motion monitor. Our concept of using an array of transmitting antennas turned out to be widely insensitive to the positioning of the test persons. A time shift between the respiratory motion curves recorded with the radar system and the motion curves from the external respiratory monitor was observed which indicates a slight difference between internal organ motion and motion detected by the external respiratory monitor. The simulations were in good accordance with the measurements. Conclusions: A continuous wave radar operating in the near field of the antennas can be used to determine the respiratory motion of humans accurately. In contrast to trigger systems used today, the radar system is able to measure motion inside the body. If such a monitor was routinely available in clinical CT, it would be possible optimizing the scan start with respect to the respiratory state of the patient. Breathing commands would potentially widely be avoided, and as far as uncooperative patients or children are concerned, less sedation might be necessary. Further applications of the radar system could be in radiation therapy or interventional imaging for instance. [ABSTRACT FROM AUTHOR]

It is demonstrated that the traditional method, in widespread use on Next Generation Weather Radar (NEXRAD) and other radar systems, to compute echo-top heights results in both under- and overestimates. It is proposed that echo tops be computed by interpolating between elevation scans that bracket the echo-top threshold. The traditional and proposed techniques are evaluated using simulated radar samples of a modeled thunderstorm and by sampling a high-resolution range-height indicator (RHI) of a real thunderstorm. It is shown that the proposed method results in smaller errors when higher-elevation scans are available. [ABSTRACT FROM AUTHOR]

Air surveillance radar tracking systems present a variety of known problems related to uncertainty and lack of accurately in radar measurements used as source in these systems. In this work, we feature the theoretical aspects of a tracking algorithm based on neural network paradigm where, from discrete measurements provided by surveillance radar, the objective will be to estimate the target state for tracking purposes as accuracy as possible. The absence of an optimal statistical solution makes the featured neural network attractive despite the availability of complex and well-known filtering algorithms. Neural networks exhibit universal mapping capabilities that allow them to be used as a control tool for capturing hidden information about models learned from a dataset. We use these capabilities to let the network learn, not only from the received radar measurement information, but also from the aircraft maneuvering context, contextual information, where tracking application is working, taking into account this new contextual information which could be obtained from predefined, commonly used, and well-known aircraft trajectories. In this case study, the proposed solution is applied to a typical air combat maneuvering, a dogfight, a form of aerial combat between fighter aircraft. Advantages of integrating contextual information in a neural network tracking approach are demonstrated. [ABSTRACT FROM AUTHOR]