High-resolution Radar Research Articles

Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data

AbstractOver half of the recent mass loss from the Greenland ice sheet, and its associated contribution to global sea level rise, can be attributed to increased surface meltwater runoff, with the remainder a result of dynamical processes such as calving and ice discharge. It is therefore important to quantify the distribution of melting on the ice sheet if we are to adequately understand past ice sheet change and make predictions for the future. In this article, we present a novel semi-empirical approach for characterising ice sheet surface conditions using high-resolution synthetic aperture radar (SAR) backscatter data from the Sentinel-1 satellite. We apply a state-space model to nine sites within North-East Greenland to identify changes in SAR backscatter, and we attribute these to different surface types with reference to optical satellite imagery and meteorological data. A set of decision-making rules for labelling ice sheet melting states are determined based on this analysis and subsequently applied to previously unseen sites. We show that our method performs well in (1) recognising some of the ice sheet surface types such as snow and dark ice and (2) determining whether the surface is melting or not melting. Sentinel-1 SAR data are of high spatial resolution; thus, in developing a method to identify the state of the surface from these data, we improve our capability to understand the variation of ice sheet melting across time and space.

Clutter Cancellation Methods for Small Target Detection Using High-Resolution W-band Radar.

Drones are currently being used for various applications. However, the detection of drones for defense or security purposes has become problematic because of the use of plastic materials and the small size of these drones. Any drone can be placed under surveillance to accurately determine its position by collecting high-resolution data using various detectors such as the radar system proposed in this paper. The W-band radar has a high carrier frequency, which makes it easy to design a wide bandwidth system, and the wideband FMCW signal is suitable for creating high resolution images from a distance. Unfortunately, the huge amounts of data gathered in this way also contain clutter (such as background data and noise) that is usually generated from unstable radar systems and complex environmental factors, and which frequently gives rise to distorted data. Accurate extraction of the position of the target from this big data requires the clutter to be suppressed and canceled, but conventional clutter cancellation methods are not suitable. Four clutter cancellation algorithms are assessed and compared: standard deviation, adaptive least mean squares (LMS), recursive least squares (RLS), and the proposed LMS. The proposed LMS has combined LMS with the standard deviation method. First, the big data pertaining to the target position is collected using the W-band radar system. Subsequently, the target position is calculated by applying these algorithms. The performance of the proposed algorithms is measured and compared to that of the other three algorithms by conducting outdoor experiments.

Features of the trajectory signal processing in the distributed radar when rotating the joint set of transmitting and receiving antennas

Increasing the resolution of ground survey radar systems (radars) makes it possible to obtain images of ground objects and the observed surface with high quality. At the same time, as it is known, the most difficult is to provide high resolution radar by angular coordinate. The method based on synthesis of artificial aperture allows to increase significantly the angular resolution of airborne radar. The capabilities of a radar station with synthesis of antenna aperture (SAR) in angular resolution are determined by the structure of the spatial and temporal trajectory signal formed in the process of synthesis. In this case, the resolution is determined by the angular size of the synthesized aperture. The purpose of this article is to consider the features of the trajectory signal structure and its processing during synthesis of an artificial antenna aperture in a distributed radar station consisting of a set of two receiving and one receiving-transmitting modules rotating in a circle. It is required to analyze the trajectory signal structure and evaluate the potentially achievable angular resolution capabilities of a distributed radar station when synthesizing an artificial antenna aperture under conditions of rotation of a spatially spaced set of phase centers of real antennas. To analyze the structure of the trajectory signal generated in a distributed radar station, we have considered the geometry of the problem, because it determines the change in the current distance between the phase center of the antennas and the observation object and is the main generating factor of the arising modulation of this signal. An informative tool for analyzing the characteristics of radar systems with synthesis of antenna aperture by potential resolution, is the analysis of the output response of the optimal trajectory signal processing system. The direct convolution method has been used to obtain the responses. Evaluation of the efficiency of coherent processing of the trajectory signals formed at the separated reception points has been carried out by computer simulation. From the simulation results presented in the figures in the article, it can be seen that the coherent processing of the trajectory signal formed at the separated receiving points during rotation of the phase center of real antennas, allows to increase the azimuth resolution of the radar. Thus, the results presented in the article show that rotation of the separated phase centers of the antennas of the receiving and receiving and transmitting modules within a range of up to 120 degrees provides the formation of a synthesized aperture when processing the trajectory signal from the observed target at each receiving point. The best azimuth resolution is obtained by processing the trajectory signal at the location of the transceiver module antenna. When coherent processing of trajectory signals formed at separated receiving points is performed together, the azimuth resolution of the radar station increases by about two times compared to the case of their separate handling.

Accuracy Improvement of High-Resolution Wide-Swath Spaceborne Synthetic Aperture Radar Imaging with Low Pule Repetition Frequency

For a single-channel spaceborne synthetic aperture radar (SAR), the usage of a low pulse repetition frequency (PRF) is an effective technical way to extend the range swath. The sub-aperture imaging strategy is usually used to solve the problem of azimuth spectrum aliasing under the condition of a low PRF. However, the required up-sampling processing before the coherent synthesis of sub-images will lead to spectrum discontinuity between adjacent sub-images, which leads to an obvious grating lobe phenomenon after the process of sub-image synthesis, resulting in a significant decrease in image quality. For this issue, a high-resolution wide-swath (HRWS) imaging algorithm for a spaceborne SAR with a low PRF is proposed in this paper based on optimal spectrum shift processing. First, each sub-aperture is imaged using the typical range migration algorithm (RMA), and then all sub-images are up-sampled at the same time. Then, based on the criterion of the minimum grating lobe, the optimal spectrum shift is estimated. Finally, the spectrum of all sub-images is shifted and then all the shifted sub-images are synthesized coherently. The simulation data processing results verify the effectiveness of the proposed method.

Estimation of lunar surface roughness using Chandrayaan-2 full-polarimetric DFSAR data

Polarimetric radar backscattering coefficients depend on the impinging frequency, dielectric constant, incidence angle, polarization, and surface roughness. Therefore, surface roughness can be estimated using high-resolution polarimetric radar datasets. For this purpose, we examine the utility of model-based and eigenvalue-based decomposition approaches in this work. While using model-based decomposition, the dielectric constant is estimated at the outset. Thereafter, the rms height is derived from a scalar multiplier that models surface roughness. We also propose a modified single-bounce eigenvalue relative difference (SERD) and establish that it is a better indicator of surface roughness than the circular polarization coherence and the original SERD for non-reflection-symmetric lunar terrain. The Chandrayaan-2 Dual Frequency Synthetic Aperture Radar (DFSAR) datasets acquired over three Apollo mission landing sites are used for demonstration.

Multi-headed deep learning-based estimator for correlated-SIRV Pareto type II distributed clutter

This paper deals with the problem of estimating the parameters of heavy-tailed sea clutter in high-resolution radar, when the clutter is modeled by the correlated Pareto type II distribution. Existing estimators based on the maximum likelihood (ML) approach, integer-order moments (IOM) approach, fractional-order moments (FOM), and log-moments (log-MoM) have shown to be sensitive to changes in data correlation. In this work, we resort to a deep learning (DL) approach based on a multi-headed architecture to overcome this problem. Offline training of the artificial neural networks (ANN) is carried out by using several combinations of the clutter parameters, with different correlation degrees. To assess the performance of the proposed estimator, we resort to Monte Carlo simulation, and we observed that it has superior performance over existing approaches in terms of estimation mean square error (MSE) and robustness to changes of the clutter correlation coefficient.

Nowcasting Multiparameter Phased-Array Weather Radar (MP-PAWR) Echoes of Localized Heavy Precipitation Using a 3D Recurrent Neural Network Trained with an Adversarial Technique

Abstract We present nowcasts of sudden heavy rains on meso-γ scales (2–20 km) using the high spatiotemporal resolution of a multiparameter phased-array weather radar (MP-PAWR) sensitive to rain droplets. The onset of typical storms is successfully predicted with 10-min lead time, i.e., the current predictability limit of rainfall caused by individual convective cores. A supervised recurrent neural network based on long short-term memory with 3D spatial convolutions (RN3D) is used to account for the horizontal and vertical changes of the convective cells with a time resolution of 30 s. The model uses radar reflectivity at horizontal polarization ZH and the differential reflectivity. The input parameters are defined in a volume of 64 × 64 × 8 km3 with the lowest level at 1.9 km and a resolution of 0.4 × 0.4 × 0.25 km3. The prediction is a 10-min sequence of ZH at the lowest grid level. The model is trained with a large number of observations of summer 2020 and an adversarial technique. RN3D is tested with different types of rapidly evolving localized heavy rainfalls of summers 2018 and 2019. The model performance is compared to that of an advection model for 3D extrapolation of PAWR echoes (A3DM). RN3D better predicts the formation and dissipation of precipitation. However, RN3D tends to underestimate heavy rainfall especially when the storm is well developed. In this phase of the storm, A3DM nowcast scores are found slightly higher. The high skill of RN3D to predict the onset of sudden localized rainfall is illustrated with an example for which RN3D outperforms the operational precipitation nowcasting system of Japan Meteorological Agency (JMA). Significance Statement Temporal extrapolation of radar observations is a means of nowcasting sudden heavy rains, i.e., forecasts with a few tens of minutes and a high spatial resolution better than 500 m. They are necessary to set up warning systems to anticipate damage to infrastructure and reduce the fatalities these storms cause. It is a difficult task due to the storm suddenness, restricted area, and nonlinear behavior that are not well captured by current operational observation and numerical systems. In this study, we use a new high-resolution weather radar with polarimetric information and a 3D recurrent neural network to improve 10-min nowcasts, the current limit of operational systems. This is a first and essential step before applying such a method for increasing the prediction lead time.

High-resolution radar ranging based on the ultra-wideband chaotic optoelectronic oscillator.

A high-resolution radar ranging scheme is proposed and demonstrated based on the ultra-wideband chaotic optoelectronic oscillator (OEO). Through biasing the electro-optic intensity modulator near its minimum transmission point, high-dimensional chaotic signals with flat spectra and low time-delayed signatures can be generated in the OEO, which are favorable for increasing the ranging resolution and the confidentiality. In the experiment, the optimized broadband OEO generates a high-dimensional chaotic signal with a flat spectrum in the frequency range of 2 GHz to 16 GHz and a high permutation entropy of 0.9754. This chaotic signal is used to achieve multiple target ranging, where a ranging resolution of 1.4 cm is realized.

A multi-scale algorithm for the NISAR mission high-resolution soil moisture product

This study proposes a multi-scale soil moisture algorithm for the upcoming NASA-ISRO SAR (NISAR) mission to estimate high-resolution (200 [m]) soil moisture (the water content of the soil). The algorithm takes advantage of the high-resolution (∼10 [m]) synthetic aperture radar (SAR) backscatter and coarse resolution modeled/reanalysis soil moisture products (∼ 9 [km]) to create a high-resolution (200 [m]) soil moisture product at a global extent. The end goal of the algorithm is to remove dependencies on any complex modeling, tedious retrieval steps, or multiple ancillary data needs, and subsequently decrease the degrees of freedom to achieve optimal accuracy in soil moisture retrievals. The use of modeled/reanalysis soil moisture products with high temporal resolution gives an added advantage in reducing the temporal mismatch between the two different inputs used in the algorithm. In this study, the proposed algorithm is tested using L-band UAVSAR backscatter (σ°) data and Advanced Land Observing Satellite −2 (ALOS-2) SAR σ° as a substitute for the NISAR L-band SAR observations. The algorithm uses the L-band SAR σ° to disaggregate coarse resolution (∼9 [km]) reanalysis soil moisture of the European Centre for Medium-Range Weather Forecast (ECMWF) to a high-resolution of ∼200 [m] soil moisture product. The potential of the algorithm is demonstrated over three sites in different hydroclimatic regions of the world, such as India, the USA, and Canada. The high-resolution soil moisture estimates were compared with the in-situ soil moisture measurements available for three sites (North India, Southern California, and Carman, Manitoba, Canada). In North India, in-situ measurements are from paddy crops with high vegetation water content, the unbiased root-mean-square-error (ubRMSE) for the high-resolution soil moisture retrievals was found to be 0.036 [m3/m3] with a bias of −0.051 [m3/m3]. For the southern California site, the validation statistics shows low ubRMSE of 0.027 [m3/m3] and a low bias of 0.016 [m3/m3]. At the Carman, Manitoba test site of Canada, where in-situ soil measurements were available for multiple crop types, the comparison shows that the ubRMSE for all the crop types lies below 0.05 [m3/m3] with an average bias of <0.07 [m3/m3]. The result confirms that the proposed algorithm meets the NISAR mission's accuracy goals, i.e., 0.06 [m3/m3] ubRMSE over areas with vegetation water content (VWC) below 5 [kg/m2]. Rigorous validation work will still need to be carried out in the future based on the availability of L-band SAR datasets and after the launch of the NISAR satellite.

Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction

In ground static target detection, polarimetric high-resolution radar can distinguish the target from the strong ground clutter by reducing the clutter power in the range cell and providing additional polarimetric features. Since the energy of a target is split over several range cells, the resulting detection problem is called polarimetric range extended target (RET) detection, where all target scattering centers should be considered. In this paper, we propose a novel polarimetric RET detection method via adaptive range weighted feature extraction. Specifically, polarimetric features of range cells are extracted, and a pretrained attention-mechanism-based module is used to adaptively calculate range cells weights, which are used to accumulate the range cells features as detection statistics. While calculating weights, both amplitude and polarimetric features are considered. This method can make the most of polarization information and improve the accumulation effect, thus increasing the discrimination between targets and clutter. The effectiveness of the proposed method is verified compared to both popular energy-domain detection methods and existing feature-domain detection methods, and the results show that our method exhibits superior detection performance. Moreover, we further analyze our method on different target models and different clutter distributions to prove that our method is suitable for different types of targets and clutter.

A Marine Small-Targets Classification Algorithm Based on Improved Convolutional Neural Networks

Deep learning, especially convolutional neural network (CNN) techniques, has been shown to have superior performance in ship classification, as have small-target recognition studies in safety inspections of hydraulic structures such as ports and dams. High-resolution synthetic aperture radar (SAR)-based maritime ship classification plays an increasingly important role in marine surveillance, marine rescue, and maritime ship management. To improve ship classification accuracy and training efficiency, we proposed a CNN-based ship classification method. Firstly, the image characteristics of different ship structures and the materials of ship SAR images were analyzed. We then constructed a ship SAR image dataset and performed preprocessing operations such as averaging. Combined with a classic neural network structure, we created a new convolutional module, namely, the Inception-Residual Controller (IRC) module. A convolutional neural network was built based on the IRC module to extract image features and establish a ship classification model. Finally, we conducted simulation experiments for ship classification and analyzed the experimental results for comparison. The experimental results showed that the average accuracy of ship classification of the model in this paper reached 98.71%, which was approximately 3% more accurate than the traditional network model and approximately 1% more accurate compared with other recently improved models. The new module also performed well in evaluation metrics, such as the recall rate, with accurate classifications. The model could satisfactorily describe different ship types. Therefore, it could be applied to marine ship classification management with the possibility of being extended to hydraulic building target recognition tasks.

A 10-kW Output Power Ka- to W-Band Klystron Harmonic Generator

The growing interest in high-power millimeter (mm)-wave amplifiers for various applications, such as high-resolution radar, active mm-wave imaging and atmospheric sensing, near object analysis, and 5G communications leading to novel solutions through which the researchers can overcome difficulty, complexity, and high expenses involved in fabrication technology of conventional vacuum electron devices including gyrotron, klystron, and traveling wave tube at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</i> -band frequency. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ka</i> -band to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</i> -band klystron harmonics generator (KHG) can be an ideal solution to overcome these challenges. This article presents the design and simulation of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ka</i> -band to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</i> -band KHG, which triples input frequency and amplifies third harmonics. In the design of the beam–wave interaction region, two methods, including the Friedlander method and the core oscillation method, have been used. In the output cavity, TM130 higher mode was applied. Using high-order mode increases the dimensions of the output cavity and decreases mechanical complexity and cost. The results gained from simulation in particle-in-cell computer simulation technology (CST) solver showed that the output power could reach higher than 10.7 kW with 53-dB gain and 24% beam efficiency in TM310 output cavity mode. The electron beam voltage is 45 kV with a 1-A beam current.

Improving the urban features classification accuracy by fusion of optical and radar high spatial resolution images

Improving the urban features classification accuracy by fusion of optical and radar high spatial resolution images

Track-Before-Detect Using an Airborne Multichannel Radar in the Maritime Domain

This paper explores methods for detection and tracking of maritime targets using an airborne scanning multi-channel radar, operating at high altitudes. The main technical difficulties include: (a) the non-stationary and non-Gaussian nature of sea clutter when using a high-resolution airborne radar, and (b) low target signal-to-interference ratios due to higher grazing angles. It is proposed to carry out processing in two stages. First, space time adaptive processing (STAP) is applied to exploit the spatial and temporal degrees of freedom available in the multi-channel radar. Subsequently, target detection and tracking is performed using the track-before-detect method, applied to the STAP output. For the sake of comparison, a more traditional tracking method is also developed, where a detection threshold is applied to the STAP output, to obtain point measurements, subsequently processed via an extended target tracking methodology. The error performance of the proposed methodology is presented, using high-fidelity radar data generation.

Delineating Polynya Area Using Active and Passive Microwave Sensors for the Western Ross Sea Sector of Antarctica

A polynya is an area of open water or reduced concentration of sea ice surrounded by either concentrated sea ice or land ice. They are often seen as sites of intense ocean–atmosphere heat exchange and as ice production factories. Given their importance, it is crucial to quantify the accuracy of satellite-derived polynya information. Polynyas in their early evolution phase are generally narrow and occur at scales likely too fine to be detected by widely used passive microwave (PMW) radiometric sensors. We derived 40 m scale polynya information over the western Ross Sea from high-resolution Synthetic Aperture Radar (SAR) Sentinel-1 C-band data and examined discrepancies with larger-scale estimates. We utilized two automated algorithms, supervised (a rule-based approach) and unsupervised (a combination of texture analysis with k-means clustering), to accurately identify the polynya areas. We generated data for validation using Sentinel-1 data at instances where polynyas can be visually delineated. Results from PMW sensors (NSIDC and AMSR2) and SAR-based algorithms (rule-based and texture-based) are compared with manually delineated polynya areas obtained through Sentinel-1. Analysis using PMW sensors revealed that NSIDC overestimates larger polynyas and underestimates smaller polynyas compared to AMSR2. We were more accurately able to identify polynya presence and area using Sentinel-1 SAR observations, especially in clear cases and cases when PMW data miscalculates the polynya’s presence. Of our SAR-based algorithms, the rule-based approach was more accurate than the texture-based approach at identifying clear polynyas when validated against manually delineated regions. Altogether, we emphasize the need for finer spatio-temporal resolution data for polynya studies.

High-Resolution and Wide-Swath SAR Imaging with Space–Time Coding Array

To achieve high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) images, this paper focuses on resolving the problem of separating range-ambiguous echoes with the space–time coding (STC) array. At the modeling stage, the transmit elements and pulses of the STC array are configured with time delay and phase coding modulation, which introduces extra degrees of freedom (DOFs) in the transmit domain. To separate the echoes corresponding to different range-ambiguity regions, the equivalent transmit beamforming is performed in the two-dimensional space–frequency domain. Moreover, in order to compensate for the loss of range resolution during the beamforming progress, the frequency splicing method is proposed. At the analysis stage, the distributed target simulation is provided to demonstrate the effectiveness of obtaining HRWS SAR images in the STC radar. Additionally, the performance of resolving range ambiguity is compared with the traditional radar in terms of the range-ambiguity-to-signal ratio (RASR).

A 124-to-152-GHz Power Amplifier Exploiting Chebyshev-Type Two-Section Wideband and Low-Loss Power-Combining Technique in 28-nm CMOS

This article presents a high-power wideband power amplifier (PA) with a four-way power-combining technique for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D$</tex-math> </inline-formula> -band high-resolution radar. The power combiner is based on a two-section Chebyshev impedance converter to achieve a large bandwidth (BW) and is realized with slow wave coplanar waveguides (S-CPWs) to reduce the insertion loss and chip area. Therefore, our developed technique can provide broadband and low-loss power-combining scheme in a very compact size. A high-power PA with four-way combining has been fabricated in a 28-nm CMOS bulk technology. The unit PA uses low- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> transformer-based coupled-resonator networks to realize wideband matching. The measured results of the PA prototype demonstrate the peak gain of 22.6 dB, the saturated output power of 16.2 dBm, and the peak power added efficiency of 8.6% at 135 GHz with a 3-dB BW of 28 GHz from 124 to 152 GHz. Meanwhile, the total area of this PA die is merely 0.66 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> 0.73 mm.

A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT

Abstract. Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness and has thus been used in a range of Earth science disciplines. However, there is no single global radar data set that has a relatively long wavelength and a decades-long time span. We here provide the first long-term (since 1992), high-resolution (∼8.9 km instead of the commonly used ∼25 km resolution) monthly satellite radar backscatter data set over global land areas, called the long-term, high-resolution scatterometer (LHScat) data set, by fusing signals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3 GHz), Quick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4 GHz), and the Advanced SCATterometer (ASCAT; since 2007; C-band; 5.255 GHz). The 6-year data gap between C-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. To this end, we first rescaled the signals from different sensors, pixel by pixel. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals by modelling the signal differences from climatic variables (i.e. monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r, root mean square error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). We obtained high Pearson r values and low RMSE values at both the regional (r≥0.92, RMSE ≤ 0.11 dB, and rRMSE ≤ 0.38) and pixel levels (median r across pixels ≥ 0.64, median RMSE ≤ 0.34 dB, and median rRMSE ≤ 0.88), suggesting high accuracy for the data-merging procedure. The merged radar signals were then validated against the European Space Agency (ESA) ERS-2 data, which provide observations for a subset of global pixels until 2011, even after the failure of on-board gyroscopes in 2001. We found highly concordant monthly dynamics between the merged radar signals and the ESA ERS-2 signals, with regional Pearson r values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic. The LHScat data set (https://doi.org/10.6084/m9.figshare.20407857; Tao et al., 2023) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture with a high spatial resolution. The data set will be updated on a regular basis to include the latest images acquired by ASCAT and to include even higher spatial and temporal resolutions.

RF Glass Technology Is Going Mainstream: Review and Future Applications

Driven by the increasing demand for high-throughput communication links and high-resolution radar sensors, the development of future wireless systems pushes at ever greater operating frequencies. By analogy, high-performance computing (HPC) systems with high-bandwidth I/Os have become a mainstream solution to address multi Gbit/s data rates. Heterogeneous integration technologies play a vital role here in enhancing the performance and functional density, along with reducing the size and costs of such RF systems. In line with this trend, many passive components, which have once been monolithically integrated, are now implemented on ceramic or polymer-based packages. Besides these long-established material and process technologies, considerable efforts have also recently been devoted to the development of RF components on glass and glass-ceramics. Spurred by their low dielectric losses, extremely smooth surfaces, and excellent dimensional stability, glass technologies are emerging as a promising alternative for high-volume and high-performance RF applications. In this article, relevant glass materials and key enabling technologies are reviewed and put into context with well-established RF substrate technologies. Another focus is set on the latest glass-based packaging and interposer solutions ranging from MHz-to-THz frequencies. To showcase the development activities and practical accomplishments of the RF glass technology, also a large variety of key components is presented. Finally, the paper concludes by discussing future research and development directions of RF glass devices.

Array Design Using Genetic Algorithm for the Generation of Sum and Difference Patterns

In the field of pattern synthesis, the design of arrays to synthesis optimized sum and difference patterns in a successive manner is an important problem in most of the radars. Although several researchers published many papers in the open literature, investigations are made by the authors to design arrays of discrete radiators using a well-formulated Genetic Algorithm (GA). The practical constraints are taken into account to design an amplitude distribution to produce the sum pattern with desired side lobe levels without broadening the main beam. Such sum patterns are found to be very useful in high-resolution radars where the EMI problems are also addressed. In IFF radars, both sum and difference patterns are extremely useful to distinguish between friendly aircraft and enemy aircraft. Such patterns are conventionally produced involving two antennas. One is a directional antenna and another is an omnidirectional antenna. However, in the present work, they are generated using only one directional antenna. The patterns with a deep null in the boresight direction and difference lobes with a high slope are produced with an antiphase excitation to one-half of the array.