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The introduction of the switched affine system opens new perspectives for the control design of dc–dc converters. In order to overcome the drawbacks of the continuous-time switching control methods, several sampled-data-based switching control approaches have been proposed in the literature. However, in these works, the solution of control parameters involves a nonlinear optimization problem. In addition, the designed switching laws are nonlinear functions about the system state, which require higher computational resources. In this article, a periodic time-triggered hybrid control is proposed based on a new design framework. All the design conditions are in linear matrix inequality formulations, which can be conveniently solved by common semidefinite programming software. Moreover, the proposed switching law is a linear function about the state, reducing the online computing burden. Finally, a unified view of the classical continuous-time switching scheme and the proposed method is established. Simulation and experiment tests on a buck–boost converter demonstrate the good performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Algorithmic changes that increase beamforming speed have become increasingly relevant to processing synthetic aperture (SA) ultrasound data. In particular, beamforming SA data in a spatio-temporal frequency domain using the F-k (Stolt) migration have been shown to reduce the beamforming time by up to two orders of magnitude compared with the conventional delay-and-sum (DAS) beamforming, and it has been used in applications where large amounts of raw data make real-time frame rates difficult to attain, such as multistatic SA imaging and plane-wave Doppler imaging with large ensemble lengths. However, beamforming signals in a spatio-temporal Fourier space can require loading large blocks of data at once, making it memory-intensive and less suited for parallel (i.e., multithreaded) processing. As an alternative, we propose beamforming in a range-Doppler (RD) frequency domain using the range-Doppler algorithm (RDA) that has originally been developed for SA radar (SAR) imaging. Through simulation and phantom experiments, we show that RDA achieves similar lateral resolution and contrast compared with DAS and F-k migration. At the same time, higher axial sidelobes in RDA images can be reduced via (temporal) frequency binning. Like the F-k migration, RDA significantly reduces the overall number of computations relative to DAS, and it achieves ten times lower processing time on a single CPU. Because RDA uses only a spatial Fourier transform (FT), it requires two times less memory than the F-k migration to process the simulated multistatic data and can be executed on as many as a thousand parallel threads (compared with eight parallel threads for the F-k migration), making it more suitable for implementation on modern graphics processing units (GPUs). While RDA is not as parallelizable as DAS, it is expected to hold a significant speed advantage on devices with moderate parallel processing capabilities (up to several thousand cores), such as point-of-care and low-cost ultrasound devices. [ABSTRACT FROM AUTHOR]

In this communication, a dual-band shared-aperture reflectarray (RA) with a phased array is proposed for intersatellite links (ISLs). The Ka-band RA is integrated with an X-band phased array by a sharing mushroom patch structure. The high-frequency ratio allows the high-frequency RA and the low-frequency broadband phased array to operate independently to support high-speed communication of the geostationary orbit (GEO) relay nodes in the intersatellite network. To verify this concept, a prototype of the antenna is designed and fabricated, and its electrical performance is measured. The RA presents a peak gain of 30.1 dBi (28 GHz) and a 3 dB gain bandwidth of 11.4% (26.4–29.6 GHz). The phased array presents the peak gain of 21.7 dBi (9 GHz), and the relative bandwidth can reach 45.3% in the boresight direction. The scanning range can cover ±45°, and the H/E planes scan gain losses are less than 2.34 and 4.33 dB, respectively. [ABSTRACT FROM AUTHOR]

Acoustic-resolution photoacoustic micro- scopy (AR-PAM) system can provide 3-D images of facial tissues. The lateral resolution of AR-PAM depends on the numerical aperture (NA) of the acoustic lens and the central frequency of the ultrasonic transducer. There is a trade-off between resolution enhancement and imaging depth. The acoustic beam is tight in the acoustic focal plane but expands in the out-of-focus regions, deteriorating the resolution. High-NA AR-PAM has depth-variant resolution. Synthetic aperture focusing technique (SAFT) based on a virtual detector (VD) concept can compensate for the beam shape and improve the lateral resolution via beamforming. Although, beamforming can enhance the resolution but the lateral resolution in the focal plane is still limited by acoustic diffraction. Structured-illumination can shift the spatial spectrum of an image to low frequencies hence high-frequency contents can be reserved to overcome the diffraction limit. Conventional structured-illumination via using a three-phase-shifting method can improve the resolution by two folds. Here, a modified phase-shifting method is used to generate the second harmonic of the fringes and double the spectral shift. In this idea, higher frequency information compared to the three-phase shifting method can fall into the band-limited system response. The modified phase-shifting method expands the spatial bandwidth and increases the lateral resolution by five folds. The mathematical relations and the theory are discussed in the context. Tungsten filament result shows resolution improvement from 44.6 $\mu \text{m}$ to 11.3 $\mu \text{m}$ by the modified structured illumination. In vivo and ex vivo experimental results validate the system performance. [ABSTRACT FROM AUTHOR]

Speckle or multiplicative noise is a critical issue in coherence-based imaging systems, such as synthetic aperture radar and optical coherence tomography. Existence of speckle noise considerably limits the applicability of such systems by degrading their performance. On the other hand, the sophistications that arise in the study of multiplicative noise have so far impeded theoretical analysis of such imaging systems. As a result, the current acquisition technology relies on heuristic solutions, such as oversampling the signal and converting the problem into a denoising problem with multiplicative noise. This paper attempts to bridge the gap between theory and practice by providing the first theoretical analysis of such systems. To achieve this goal the log-likelihood function corresponding to measurement systems with speckle noise is characterized. Then employing compression codes to model the source structure, for the case of under-sampled measurements, a compression-based maximum likelihood recovery method is proposed. The mean squared error (MSE) performance of the proposed method is characterized and is shown to scale as $O\left({\sqrt {\frac{k \log n }{ m}}}\right)$ , where $k$ , $m$ and $n$ denote the intrinsic dimension of the signal class according to the compression code, the number of observations, and the ambient dimension of the signal, respectively. This result, while in contrast to imaging systems with additive noise in which MSE scales as $O\left({{\frac{k \log n }{ m}}}\right)$ , suggests that if the signal class is structured (i.e., $k \ll n$), accurate recovery of a signal from under-determined measurements is still feasible, even in the presence of speckle noise. Simulation results are presented that suggest image recovery under multiplicative noise is inherently more challenging than additive noise, and that the derived theoretical results are sharp. [ABSTRACT FROM AUTHOR]

Top-orthogonal-to-bottom electrode (TOBE) arrays, also known as row–column arrays, have shown great promise as an alternative to fully wired 2-D arrays, owing to a considerable reduction in channels. Novel imaging schemes with bias-switchable TOBE arrays were previously shown to offer promise compared with previous nonbias-switchable row–column imaging schemes and compared with previously developed explososcan methods, however, they required significant coherent compounding. Here, we introduce ultrafast orthogonal row–column electronic scanning (uFORCES), an ultrafast coded synthetic aperture imaging method. Unlike its FORCES predecessor, uFORCES can achieve coherent compounding with only a few transmit events and may, thus, be more robust to tissue motion. We demonstrate through simulations that uFORCES can potentially offer improved resolution compared with the matrix probes having beamformers constrained by the paraxial approximation. Also, unlike current matrix probe technology incorporating microbeamforming, uFORCES with bias-switchable TOBE arrays can achieve ultrafast imaging at thousands of frames per second using only row and column addressing. We also demonstrate the experimental implementation of uFORCES using a fabricated $128 \times 128$ electrostrictive TOBE array on a crossed 25- $\mu \text{m}$ gold wire phantom and a tissue-mimicking phantom. The potential for improved resolution and ultrafast imaging with uFORCES could enable new essential imaging capabilities for clinical and preclinical ultrasound. [ABSTRACT FROM AUTHOR]

Row–column (RC) arrays have the potential to yield full 3-D ultrasound imaging with a greatly reduced number of elements compared to fully populated arrays. They, however, have several challenges due to their special geometry. This review article summarizes the current literature for RC imaging and demonstrates that full anatomic and functional imaging can attain a high quality using synthetic aperture (SA) sequences and modified delay-and-sum beamforming. Resolution can approach the diffraction limit with an isotropic resolution of half a wavelength with low sidelobe levels, and the field of view can be expanded by using convex or lensed RC probes. GPU beamforming allows for three orthogonal planes to be beamformed at 30 Hz, providing near real-time imaging ideal for positioning the probe and improving the operator’s workflow. Functional imaging is also attainable using transverse oscillation and dedicated SA sequence for tensor velocity imaging for revealing the full 3-D velocity vector as a function of spatial position and time for both blood velocity and tissue motion estimation. Using RC arrays with commercial contrast agents can reveal super-resolution imaging (SRI) with isotropic resolution below $20~ \mu \text{m}$. RC arrays can, thus, yield full 3-D imaging at high resolution, contrast, and volumetric rates for both anatomic and functional imaging with the same number of receive channels as current commercial 1-D arrays. [ABSTRACT FROM AUTHOR]

A novel method for fabricating a modular, kerf-minimizing histotripsy phased array was developed and tested. The method utilizes arbitrarily shaped elements, 3-D printing, water jet cutting, and a thin, 125- $\mu \text{m}$ electrically insulating epoxy coating to maximize aperture utilization while allowing for replacement of individual transducer modules. The method was used to fabricate a 750-kHz truncated circular aperture array (165 mm $\times234$ mm) transducer with a focal length of 142 mm. The aperture was segmented into 260 arc-shaped modular elements, each approximately 11.5 mm $\times11.5$ mm, arranged in concentric rings. The resulting aperture utilization was 92%. The full-width-half-maximum (FWHM) focal zone of the array was measured to be 1.6 mm $\times1.1$ mm $\times4.5$ mm, and the FWHM electrical steering range was measured to be 38.5 mm $\times33$ mm $\times \,\,>$ 40 mm. The array was estimated to be capable of generating approximately 120-MPa peak negative pressure at the geometric focus. In addition, the array was used to ablate a 5-cm3 volume of tissue with electric focal steering. [ABSTRACT FROM AUTHOR]

Multitemporal synthetic aperture radar interferometry (MT-InSAR) is able to reconstruct a 3-D surface model with high precision but requires a long waiting time to get the multibaseline synthetic aperture radar (SAR) images. The array InSAR system can acquire multibaseline images in a single flight, which significantly improves the practical capability of 3-D reconstruction. However, the array InSAR system with many channels has very high complexity in both system and processing because of cross-channel calibration and decoupling. Thus, reducing the number of channels requires the investigation of the 3-D reconstruction algorithm to be suitable for sparse array InSAR images. This work proposed an asymptotic 3-D phase unwrapping (PU) algorithm for 3-D reconstruction using sparse array InSAR images, i.e., as few as three or four channels. A 2-D (space) + 1-D (baseline) PU framework is developed to improve the reliability of the 3-D PU, and a novel asymptotic strategy is proposed for the combination of the short–long baseline interferogram. Using successful unwrapping (SU) criteria, the bounds of the possible baseline combinations and the expected minimal coherence are derived, respectively. The main advantage of the proposed algorithm is the reliable PU with very sparse channels and an analysis of the possible baseline combinations. The experimental results by both the simulated and real data show that the proposed method can achieve a 3-D reconstruction using only three-pass array InSAR images and optimize the baseline design for the array InSAR system. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) is an efficient tool for global large-scale digital elevation model (DEM) generation. However, for steep terrain, the current approaches cannot stably reconstruct valid DEM products from a single-baseline InSAR image pair without an external reference DEM due to the influence of shadow/layover geometries, phase noise, and the Itoh condition limitation in the phase unwrapping (PU) process. In this article, a novel stereo-radargrammetry-assisted PU (SAPU) approach with no need for external auxiliary information is proposed to eliminate the constraint of the Itoh condition by exploiting the internal stereo-radargrammetric shifts. The method reduces the phase gradient in the interferogram and guides the PU process with automatically selected tie points. Notably, the current stereo-radargrammetry approaches will deteriorate to an incoherent state in steep terrain, hampering the reliability and accuracy of SAPU. Accordingly, we also propose an adaptive weighted subwindow-coherent stereo-radargrammetric shift estimation (AWS-CSE) method to improve the accuracy of subpixel shifts by introducing local topographic phase consistency in coherence estimation. We quantitatively validate the performance of the proposed methods based on the L-SAR 01 simulation data and three pairs of repeat-pass single-baseline Advanced Land Observing Satellite (ALOS) phased array type L-band synthetic aperture radar (PALSAR) images from different areas, comparing the results with those of various traditional and deep-learning-based PU methods. The findings suggest that the proposed methods can generate accurate DEMs from single-baseline measurements in steep terrain while avoiding additional data acquisitions. [ABSTRACT FROM AUTHOR]

In a recent study, we proposed a technique to correct aberration caused by the skull and reconstruct a transcranial B-mode image with a refraction-corrected synthetic aperture imaging (SAI) scheme. Given a sound speed map, the arrival times were calculated using a fast marching technique (FMT), which solves the Eikonal equation and, therefore, is computationally expensive for real-time imaging. In this article, we introduce a two-point ray tracing method, based on Fermat’s principle, for fast calculation of the travel times in the presence of a layered aberrator in front of the ultrasound probe. The ray tracing method along with the reconstruction technique is implemented on a graphical processing unite (GPU). The point spread function (PSF) in a wire phantom image reconstructed with the FMT and the GPU implementation was studied with numerical synthetic data and experiments with a bone-mimicking plate and a sagittally cut human skull. The numerical analysis showed that the error on travel times is less than 10% of the ultrasound temporal period at 2.5 MHz. As a result, the lateral resolution was not significantly degraded compared with images reconstructed with FMT-calculated travel times. The results using the synthetic, bone-mimicking plate, and skull dataset showed that the GPU implementation causes a lateral/axial localization error of 0.10/0.20, 0.15/0.13, and 0.26/0.32 mm compared with a reference measurement (no aberrator in front of the ultrasound probe), respectively. For an imaging depth of 70 mm, the proposed GPU implementation allows reconstructing 19 frames/s with full synthetic aperture (96 transmission events) and 32 frames/s with multiangle plane wave imaging schemes (with 11 steering angles) for a pixel size of $200~\mu \text{m}$. Finally, refraction-corrected power Doppler imaging is demonstrated with a string phantom and a bone-mimicking plate placed between the probe and the moving string. The proposed approach achieves a suitable frame rate for clinical scanning while maintaining the image quality. [ABSTRACT FROM AUTHOR]

The dc power collected by radio frequency energy harvesting (RFEH) absorbing surfaces is limited by their physical aperture. Here, a compact, conformable, and ultrathin sub-1 GHz large-area RFEH surface is proposed based on electrically small ($ka=0.58$) “wire-type” rectenna elements, with an effective area exceeding its physical aperture size. Using optimized large-signal complex source tuning, the rectifiers achieve up to 36% measured power conversion efficiency (PCE) at =20 dBm. The proposed 15 cm-diameter six-element array generates a 27.5 $\mu \text{W}$ at 1 V output from a $0.17~\mu \text{W}$ /cm2 incident power density across a 40 $\text{k}\Omega $ load. Owing to its series connection, the voltage output is boosted, and the load resistance dependence is suppressed enabling over 60% of the maximum RF-to-dc PCE to be preserved for loads between 7 and 100 $\text{k}\Omega $ , with an area-normalized figure-of-merit nearly 100% higher than previous arrays. The proposed array is integrated with a commercial DC-DC converter and demonstrated powering a Bluetooth low energy (BLE) wireless sensor node (WSN) from an unprecedented incident power density of 0.25 $\mu \text{W}$ /cm2. A practical demonstration using a commercial 3 W, 915 MHz Powercast source is presented, showing an 11 m operation range with a 1 V output and illustrating the impact of polarization mismatch on the proposed array. [ABSTRACT FROM AUTHOR]

Objective: Two-dimensional (2D) photoacoustic (PA) imaging based on array transducers provide high spatial resolution in the lateral direction by adopting receive dynamic focusing. However, the quality of PA image is often deteriorated by poor elevational resolution which is achieved by an acoustic lens. To overcome this limitation, we present a three-dimensional (3D) image reconstruction method using a commercial one-dimensional (1D) array transducer. Methods: In the method, the elevational resolution is improved by applying synthetic aperture focusing (SAF) technique along the elevational direction. For this, a commercially available 1D array transducer with an acoustic lens is modeled and appropriate synthetic focusing delay that can minimize the effect of the acoustic lens is derived by mathematical analysis. Results: From the simulation and experiment results, it was demonstrated that the proposed method can enhance the image quality of PA imaging, i.e., elevational resolution and signal-to-noise ratio (SNR). Conclusion: 3D PA images with improved elevational resolution were achieved using a clinical 1D array transducer. Significance: The presented method may be useful for clinical application such as detecting microcalcification, imaging of tumor vasculature and guidance of biopsy in real time. [ABSTRACT FROM AUTHOR]

Multiple-input and multiple-output synthetic aperture radar (MIMO SAR) is a very potential technique for high-resolution and wide-swath (HRWS) imaging due to the fact that it can provide more degrees of freedom. In this article, an innovative MIMO-HRWS SAR imaging system is introduced with segmented phase coding (SPC) waveforms. However, it is well-known that the echo separation issue is the most technical challenge for MIMO SAR. It has shown that the separation of orthogonal echoes from cross correlation interference can be achieved by scan-on-receive (SCORE). However, this method often requires large computing resources on board, especially for null-steering process. For this, we propose a two-stage spaceborne–ground echo separation scheme. This scheme is mainly divided into two steps: the first step is a real-time SCORE beamforming process on board to reduce the downlink data volume, and the second step is a bandpass–null-steering process to suppress the interfering echoes on the ground. Thus, this scheme allows for the low computing resources on board and sufficient suppression of the interference simultaneously. Following this scheme, the MIMO-HRWS SAR system enables its number of equivalent phase centers nearly double that of the azimuth multichannel SAR system, leading to higher resolution and wider swath imaging. Finally, detailed simulation experiments for the MIMO-HRWS SAR imaging system are performed to verify the practicability and feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

The spatial correlation of ice-sounding data can be used to trace internal isochronic layers and synchronize the age–depth relationship between different ice core sites, which is difficult using existing imaging methods. In this study, we propose a new algorithm to address the opportunity that applying spatial correlation to ice-sheet imaging offers. The algorithm is a spatial-correlation-based ice-sounding imaging method using fast back projection (FBP) that successfully improves the spatial correlation of imaging results with high efficiency. We give the specific steps to implement the algorithm and apply it to simulate both point targets and ice-sounding radar data to demonstrate its validity in imaging ice sheets. Furthermore, compared with two previous methods, the proposed algorithm improves the spatial correlation without degrading the ability of signal-to-noise ratio (SNR) improvement and processing efficiency. [ABSTRACT FROM AUTHOR]

Because optical and synthetic aperture radar (SAR) images are complementary, their registration has received extensive attention in joint applications. However, robust optical and SAR image registration is challenging due to substantial geometric and radiometric differences. To address this problem, we propose a fast and robust registration algorithm for optical and SAR images based on a novel feature type known as the adjacent self-similarity (ASS). The ASS feature of the pixelwise feature representation is defined to quickly and finely capture the structural features of the image. The ASS feature is extracted by using an optimized offset mean filtering method in a neighborhood of the unit pixel radius to accelerate and refine calculations and the local statistics weighted difference operation to suppress coherent speckles. Based on the ASS feature, we extract the minimum self-similarity map (SSM) and the index map, which are robust against radiometric differences and speckles. Then, based on the excellent characteristics of the two maps, we propose a feature detector based on suppressing the local nonmaximum on the minimum SSM and a novel feature descriptor based on calculating the distribution histogram of the index map in a log-polar grid. In addition, we design a rotation invariance enhancement method for the descriptor to improve the rotation invariance robustness of the algorithm. We conduct experiments with both synthetic and real image pairs. The registration results demonstrate that the proposed algorithm has good scale and rotation invariance, as well as good antinoise ability, and that the algorithm performs better than existing state-of-the-art algorithms in terms of registration robustness, accuracy, and efficiency. The registration results on two real optical and SAR image pairs with complex image scenes show the adaptability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Multimodal land cover classification (MLCC) using the optical and synthetic aperture radar (SAR) modalities has resulted in outstanding performances over using only unimodal data due to their complementary information on land properties. Previous multimodal deep learning (MDL) methods have relied on handcrafted multibranch convolutional neural networks (CNN) to extract the features of different modalities and merged them for land cover classification. However, natural-image-oriented handcrafted CNN models may not be the optimal strategies to handle remote sensing (RS) image interpretation problems, due to the huge difference in terms of imaging angles and imaging ways. Furthermore, few MDL methods have analyzed optimal combinations of hierarchical features from different modalities. In this article, we propose an efficient multimodal architecture search framework, namely, multimodal semantic consistency-based fusion architecture search ($\text{M}^{2}$ SC-FAS) in continuous search space with the gradient-based optimization method, which can not only discover optimal optical- and SAR-specific architectures according to the different characteristics of the optical and SAR images, respectively, but also realizes the search of optimal multimodal dense fusion architecture. Specifically, the semantic consistency constraint is introduced to guarantee dense fusion between hierarchical optical and SAR features with high semantic consistency and then capture the complementary performance on land properties. Finally, the basis of curriculum learning strategy is adopted on $\text{M}^{2}$ SC-FAS. Extensive experiments show superior performances of our work on three broad coregistered optical and SAR datasets. [ABSTRACT FROM AUTHOR]

Microwave photonic synthetic aperture radar (MWP-SAR) offers a larger signal bandwidth than conventional SAR, and its theoretical resolution can be improved to centimeter level. To achieve same order of magnitude resolution in the azimuth direction, long synthetic aperture is always required, which results in extremely high requirements for the accuracy of imaging procedure. Compared with the conventional SAR imaging algorithms, two major problems should be considered: 1) the scattering characteristics of target might vary from the frequency of signal and the angle of incidence, which seriously affects the coherence of received echo and 2) the imaging of the MWP-SAR system is more sensitive to motion errors and therefore requires higher accuracy of motion compensation processing. To solve the above issues, a high-precision imaging method for MWP-SAR is proposed. First, based on the attribute scattering center (ASC) model, this article analyzes the target scattering characteristics with different frequencies and incident angles. Then, according to the influence of scattering phase on MWP-SAR imaging, an optimal subaperture division algorithm is proposed to guarantee the coherence of each subaperture data and better imaging results. Furthermore, to compensate for the effects of high-order motion errors, this article proposes a motion error estimation algorithm based on subimage registration, where the full-aperture high-order motion errors can be divided into multiple linear components, and the flight trajectory of platform can be accurately reconstructed. Finally, a full-aperture time-domain high-precision imaging method is presented, and the effectiveness of the proposed formation is verified by both simulation and actual airborne MWP-SAR data processing. [ABSTRACT FROM AUTHOR]

During the observation of micromotion targets in space, inverse synthetic aperture radar usually obtains the narrowband and wideband echoes simultaneously. In order to exploit their rich information in target electromagnetic scattering, shape, structure, and motion, this article proposes a recognition method of space micromotion targets based on decision fusion. The proposed method extracts physical features from the radar cross section and the joint time–frequency distribution from narrowband echoes, while extracts the data features from high-resolution range profiles and range-instantaneous-Doppler image by convolution neural network. Finally, particle swarm optimization is adopted to decision-level fusion so as to realize high-precision recognition. The recognition results of electromagnetic simulated data under various conditions have demonstrated the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Emitter localization is an active research topic in electronic reconnaissance. Recently, passive synthetic aperture (PSA) has been used to determine the azimuth and range distance of the emitter. However, the existing methods approximate the distance equation of the spaceborne model through the airborne model, resulting in a large positioning error. In this article, an emitter localization algorithm based on PSA in the spaceborne model is proposed. First, the phase of the received signal is accumulated by using the symmetry of the Doppler history of the received signal in long synthetic aperture time, improving the azimuth positioning accuracy. The effective velocity is then used to establish the nonlinear equations of range distance. A modulus function is used to solve the equations, correcting the range positioning error in the spaceborne model. Finally, the influence of noise, satellite position error, and satellite speed error on the positioning results is analyzed. The error matrix of the proposed method in wide swath is obtained. Simulation results show that the positioning accuracy of this method is one order of magnitude higher than that of traditional single-satellite positioning methods. The effectiveness of the proposed method is verified through actual satellite experiments. [ABSTRACT FROM AUTHOR]

The 3-D ultrasound (US) imaging addresses the limitation in field-of-view (FOV) in conventional 2-D US imaging by providing 3-D viewing of the anatomy. The 3-D US imaging has been extensively adapted for diagnosis and image-guided surgical intervention. However, conventional approaches to implement 3-D US imaging require either expensive and sophisticated 2-D array transducers or external actuation mechanisms to move a 1-D array mechanically. Here, we propose a 3-D US imaging mechanism using an actuated acoustic reflector instead of the sensor elements for volume acquisition with significantly extended 3-D FOV, which can be implemented with simple hardware and compact size. To improve image quality on the elevation plane, we implemented the synthetic aperture focusing (SAF) method according to the diagonal geometry of the virtual element array in the proposed imaging mechanism for elevation beamforming. We first evaluated the proposed imaging mechanism and SAF with simulated point targets and cyst targets. The results of point targets suggested improved image quality on the elevation plane, and the results of cysts targets demonstrated a potential to improve 3-D visualization of human anatomy. We built a prototype imaging system with a 3-D FOV of 38 mm (lateral) by 38 mm (elevation) by 50 mm (axial) and collected data in imaging experiments with phantoms. Experimental data showed consistency with simulation results. The SAF method enhanced quantifying the cyst volume size in the breast mimicking phantom compared with no elevation beamforming. These results suggested that the proposed 3-D US imaging mechanism could potentially be applied in clinical scenarios. [ABSTRACT FROM AUTHOR]

Multimodal data fusion, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion, plays an important role in object recognition and classification tasks. However, existing methods pay little attention to the specificity of HSI spectral channels and the complementarity of HSI and LiDAR spatial information. In addition, the utilized feature extraction modules tend to consider the feature transmission processes among different modalities independently. Therefore, a new data fusion network named AM3Net is proposed for multimodal data classification; it includes three parts. First, an involution operator slides over the input HSI’s spectral channels, which can independently measure the contribution rate of the spectral channel of each pixel to the spectral feature tensor construction. Furthermore, the spatial information of HSI and LiDAR data is integrated and excavated in an adaptively fused, modality-oriented manner. Second, a spectral-spatial mutual-guided module is designed for the feature collaborative transmission among spectral features and spatial information, which can increase the semantic relatedness connection through adaptive, multiscale, and mutual-learning transmission. Finally, the fused spatial-spectral features are embedded into a classification module to obtain the final results, which determines whether to continue updating the network weights. Experimental evaluations on HSI-LiDAR datasets indicate that AM3Net possesses a better feature representation ability than the state-of-the-art methods. Additionally, AM3Net still maintains considerable performance when its input is replaced with multispectral and synthetic aperture radar data. The result indicates that the proposed data fusion framework is compatible with diversified data types. [ABSTRACT FROM AUTHOR]

The imaging performance of the lunar-based synthetic aperture radar (LBSAR) is susceptible to the orbital perturbation effects. In particular, the perturbations of orbital inclination and right ascension of ascending node (RAAN) could give rise to the temporally varying orbit drift of LBSAR and further lead to Doppler errors in the radio signal. As a result, the LBSAR image performance might be influenced by such effects. This study comprehensively probes into the phase error induced by perturbations of orbital inclination and RAAN, and its effects on the LBSAR imaging performance are further explored. It is found that the LBSAR imaging performance in terms of focusing quality and geometric fidelity is affected by the perturbations of orbital inclination and RAAN, wherein the deterioration of focusing quality is closely associated with the synthetic aperture time. In this regard, the azimuth resolution on a decameter level is optimum for Earth observation of LBSAR with satisfactory image quality. Regarding the geometric fidelity, the accuracy requirement for the orbit determination of the LBSAR under perturbations of the orbital inclination and RAAN is proposed. The analysis results show that the LBSAR orbit determination in terms of the position and velocity determinations is most strenuous in the z-direction. Finally, point target responses are simulated to illustrate the preceding analysis. [ABSTRACT FROM AUTHOR]

Deep learning has powerful feature extraction abilities and has achieved promising results in polarimetric synthetic aperture radar (PolSAR) image classification. However, the labeled samples of PolSAR images are generally limited, which could lead to the overfitting of deep networks and the inefficiency of deep features. To overcome this problem, in this article, we propose a complex-valued enforcing population and lifetime sparsity (CV-EPLS) model to extract nonredundant sparse features from PolSAR images. CV-EPLS achieves unsupervised learning of sparse polarimetric features with limited and unlabeled samples, including amplitude and phase information in multiple polarimetric channels. Concretely, CV-EPLS defines an activation metric function to achieve strong population sparsity. Additionally, a grid search strategy is designed to ensure that activation items are evenly distributed among the sparse targets, thus forming strong lifetime sparsity. In this way, CV-EPLS constructs the complex sparse matrices and extracts discriminative sparse features in an unsupervised way, with the dependence of features being effectively reduced. Experimental results on PolSAR images demonstrate the effectiveness of CV-EPLS in the extraction of features and its application to image classification. [ABSTRACT FROM AUTHOR]

River water extent is critical for understanding river discharge or its hydrological conditions. Although numerous methods have been proposed to map river water from either optical or synthetic aperture radar (SAR) remotely sensed images, uncertainties still exist broadly. In this study, we developed an image fusion method that integrates Landsat-8, Sentinel-1, and Sentinel-2 images simultaneously for river water mapping with two major steps. Firstly, a posterior probability support vector machine (SVM) model was adopted to generate water probability maps from each individual image; and second, a multidimensional weighted fusion method (MDWFM) was developed to fuse these probability maps. Four reaches with different characteristics were selected as case study sites. High-resolution aerial images were acquired and used as the reference to evaluate our results. We found that the fusion process not only improves the quality of river water mapping but also excludes the cloud interference. The fused river water maps become more reliable after the conflicts from difference images being solved by the proposed MDWFM method that contains a proportional conflict redistribution rule. The weighted root mean square difference was reduced to 0.066, and the area under the ROC curve reached up to 0.984. The critical success index (CSI), kappa coefficient (KC), and F-measure reached up to 0.810, 0.836, and 0.895, respectively. These stable and accurate river extent mapping results obtained through fusing multiple images with high spatial resolution (SR) (10 m) and short revisit interval (0.4–4.4 days) are of great significance for enriching the data and methodology of hydrological studies. [ABSTRACT FROM AUTHOR]

The nonlinear variation of viewing angles over a long duration causes a nonlinear initial phase of the received pulse in a passive positioning system with a single moving receiver. Typical positioning systems ignore the phase and perform incoherent accumulation of the long-time data, thus resulting in a decrease in positioning accuracy, especially at a low signal-to-noise ratio (SNR). A novel passive positioning system with a synthetic aperture technique, named synthetic aperture positioning (SAP) system, is proposed to resolve the issue. First, a new 2-D continuous sampling working model is proposed. Then, the SAP system and a cost function are given to analyze the positioning performance. Third, a positioning algorithm based on the maximum likelihood estimation (MLE) is studied to handle the cost function and position the emitter. Simulation and experimental results verify the validity and effectiveness of the proposed SAP system. [ABSTRACT FROM AUTHOR]

In recent years, synthetic aperture radar (SAR) ship detection methods based on convolutional neural networks (CNNs) have attracted wide attention in remote sensing fields. However, these methods require a large number of labeled SAR images to train the network, where labeling for SAR images is more expensive and time-consuming than that for optical images. To address the problem of lacking labeled SAR images, in this article, we proposed an unsupervised domain adaptation (UDA) framework based on progressive transfer for SAR ship detection by transferring knowledge from the optical domain to the SAR domain. Due to the prominent difference between the optical and SAR images, our approach progressively transfers knowledge from three levels: pixel level, feature level, and prediction level. At the pixel level, considering the difference in imaging mechanism, we propose a special data augmentation method for ship targets and build the generator with skip connection based on generative adversarial networks (GANs) to generate transition domain, which can reduce the appearance discrepancy between the optical and SAR images. At the feature level, the detector is trained to learn the domain-invariant features with adversarial alignment. At the prediction level, we further use the predicted pseudo-labels obtained by the feature-aligned detector to learn more discriminative features of the SAR images directly and propose the robust self-training (RST) method to reduce the influence of noisy pseudo-labels on detector training. Specially, RST is formulated as a loss minimization problem for object detection. The experimental results based on the domain adaptation from optical dataset to SAR dataset demonstrate that our approach achieves superior SAR ship detection performance with unlabeled SAR images. [ABSTRACT FROM AUTHOR]

For the near-field synthetic aperture interferometric radiometer (SAIR), the Fourier transform relationship between the visibility function and the near-field brightness temperature (BT) distribution is not valid. It is a challenging task for near-field SAIR imaging to realize very close-range accurate imaging with a large field of view (FOV). This article presents a new SAIR near-field imaging algorithm based on the angular spectrum theory to realize the passive millimeter-wave (PMMW) imaging, called synthetic-angular-spectrum imaging (SASI) algorithm. This SASI algorithm mainly addresses data samples of a 4-D visibility function acquired from planar arrays. First, we invert 4-D visibility samples into the angular spectrum domain via the Fourier transformation. The obtained angular spectrum data cannot be directly used to estimate the near-field BT distribution. Second, a dedicated phase factor compensation is adopted for handling this problem. The dimension-reducing accumulation is employed to generate the synthetic angular spectrum (SAS) of near-field BT distribution. Finally, we reconstruct the BT image by using the generated SAS data. Simulation and experiment results show that the presented SASI algorithm has the superiority on image reconstruction quality, especially for the off-axis regions, compared with the existing near-field SAIR imaging methods. [ABSTRACT FROM AUTHOR]

In this study, we propose a generalized algorithm for far-field radar cross section determination by using 3-D synthetic aperture imaging with arbitrary antenna-scanning surfaces. This method belongs to a class of techniques called image-based near-field-to-far-field transformation. The previous image-based approaches have been formulated based on a specific antenna-scanning trajectory or surface, such as a line, plane, circle, cylinder, and sphere; the majority of these approaches consider 2-D radar images to determine the azimuth radar cross section. We generalize the conventional image-based technique to accommodate an arbitrary antenna-scanning surface and consider a 3-D radar image for radar cross section prediction in both the azimuth and zenith directions. We validate the proposed algorithm by performing numerical simulations and anechoic chamber measurements. [ABSTRACT FROM AUTHOR]

This article proposes a new synthetic aperture radar (SAR), named as panoramic SAR, based on a combination of linear and rotational SARs, by which a large 360° panoramic view of the observed scene can be reconstructed. First, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The combined movement constitutes a 2-D synthetic aperture, and thus higher imaging resolutions can be obtained. The corresponding resolution analysis and the sampling criteria are discussed accordingly. Then, a novel dynamic piecewise compensation (DPC) algorithm, a recursive imaging process, is proposed to reduce the processing complexity significantly. The imaging implementation and the complexity are also studied respectively. Finally, a prototype of panoramic SAR is built based on an frequency-modulated continuous wave (FMCW) radar and a moving platform, and the simulation and experimental results are provided to validate the proposed panoramic SAR principle and the DPC algorithm. [ABSTRACT FROM AUTHOR]

Unsupervised methods play an essential role in polarimetric synthetic aperture radar (PolSAR) image classification, where labeled data are difficult to obtain. However, there is still a large gap between existing unsupervised learning methods and supervised learning methods. Without the semantic constraints of labeled data, pixels within the same category are often misclassified into different categories, leaving the output to be messy. To address the previous issue, this article proposes a fully unsupervised pipeline for training convolutional neural networks (CNNs). The pipeline combines low-level superpixels and high-level CNN semantic features for high-quality pseudolabel generation. It effectively eliminates the misclassified pixels by voting within the superpixel blob while preserving the sharpness of edges. With the training process of the model, the quality of the generated labels is getting improved. Experiments on airborne [experimental airborne SAR system from Germany (ESAR)/airborne synthetic aperture radar from America (AIRSAR)] and spaceborne (RadarSat2) PolSAR images prove the effectiveness of the proposed method (measured with overall accuracy (OA), average accuracy (AA), and Kappa metrics). Our method outperforms the previous unsupervised methods (H/alpha-Wishart, SM-Wishart, FDD-H, DEC, and VQC-CAE) with a large margin and even has comparable performance to the supervised CNN model [fully CNN (FCN)]. [ABSTRACT FROM AUTHOR]

A 360° video synthetic aperture radar (VideoSAR) appears as an interactive observation capability to understand the dynamic scenarios. VideoSAR system mounted on an unmanned aerial vehicle (UAV) delivers a highly flexible and cost-effective approach. In this letter, the UAV-borne repeat-pass circular stripmap (CSM) VideoSAR observation is formulated in the format of video area overflown to enhance the wide-area surveillance capability. A completely two-dimensional interpolation-free polar format algorithm is employed, especially in terms of repeat-pass CSM-VideoSAR configuration. We conduct the repeat-pass CSM-VideoSAR experiments on multirotor UAV-borne system in the interests of imaging demonstration and promoting the inspirations on the related open issues. [ABSTRACT FROM AUTHOR]

We investigate methods to improve the detection of point scatterers in ultrasound imaging using the standard delay-and-sum (DAS) image as our starting point. An optimized whitening transform can increase the spatial resolution of the image. By splitting an image’s frequency spectrum into many subsets using the multilook technique, we can exploit the coherent properties of a point scatterer. We present three new multilook methods and evaluate their effect on point detection. The performances are compared to DAS using synthetic aperture Field II simulations of a point scatterer in uniform speckle background. The results show that optimized prewhitening of the images can significantly improve the point detection. The multilook methods have the potential to improve the detection performance further when a sufficient number of looks are used. If prior knowledge about the optimal spectrum limits is unavailable and a nonoptimal prewhitening is applied, applying that the new multilook methods can considerably improve the point detection. [ABSTRACT FROM AUTHOR]

High-frequency endoscopic ultrasound (HFEUS) imaging is an important tool commonly used in clinical practice for imaging hollow organs. The virtual source synthetic aperture (VSSA) method is effective in improving the imaging quality of HFEUS. However, interference from the motor control unit severely affects the accuracy of the conventional delay and sum (DAS) method, thus compromising the effectiveness of VSSA. In this article, a new computational method based on phase correction was proposed to overcome these shortcomings, which is named phase-corrected-and-sum (PCAS). Meanwhile, the parameters of coherence factor weighting (CFW) can be obtained from the correlation coefficient of the superimposed signals to further increase the imaging quality. Three kinds of imaging experiments were designed to evaluate the proposed method. Compared with the conventional method, the results show that the PCAS-CFW method improves the lateral resolution by about 10% and the contrast-to-noise ratio (CNR) by about 44%. Therefore, this proposed method is capable of significantly improving HFEUS image quality, and this method can be easily integrated into current HFEUS imaging systems, showing great potential for clinical applications. [ABSTRACT FROM AUTHOR]

An Interview with Ana Garcia Armada, Institute of Electrical and Electronics Engineers (IEEE) Communications Society (ComSoc) Vice President for Member and Global Activities (MGA), is presented. Topics include low latency for interactive communication and novel protocols for machine communication; and member companies and academia have been researching for 5G wireless communication system.

In this article, we report on SwissSPAD3 (SS3), a 500 $\times$ 500 pixel single-photon avalanche diode (SPAD) array, fabricated in 0.18- $\mu \text{m}$ CMOS technology. In this sensor, we introduce a novel dual-gate architecture with two contiguous temporal windows, or gates, guaranteed by the circuit architecture to be nonoverlapping and covering the totality of the sensor’s exposure period. The gates can be adjusted with a temporal resolution of 17.9 ps, and the minimum measured gate width is 0.99 ns; to our knowledge, the shortest reported to date among large-format SPAD imagers. In the dual-channel mode, the burst frame rate is 49.8 and 97.7 kframes/s in the single-channel mode. A 2690-MB/s PCI express (PCIe) interface has been added to the data acquisition framework, enabling continuous operation at approximately 44 and 88 kframes/s. Due to optimizations of the gate-signal tree, we achieved a significant reduction to gate skew and gate width variation, which is negligible with respect to the SPAD temporal jitter. These improvements, along with sub-10-cps dark count rate (DCR) per pixel and 50% maximum photon detection probability (PDP), result in a sensor particularly well suited for fast acquisition fluorescence lifetime imaging microscopy (FLIM) experiments, for which we demonstrate reduced dispersion versus a single-gated sensor. [ABSTRACT FROM AUTHOR]

Optical and synthetic aperture radar (SAR) image registration is important for multimodal remote sensing image information fusion. Recently, deep matching networks have shown better performances than traditional methods of image matching. However, due to significant differences between optical and SAR images, the performances of existing deep learning methods still need to be further improved. This article proposes a self-distillation feature learning network (SDNet) for optical and SAR image registration, improving performance from network structure and network optimization. First, we explore the impact of different weight-sharing strategies on optical and SAR image matching. Then, we design a partially unshared feature learning network for multimodal image feature learning. It has fewer parameters than the fully unshared network and has more flexibility than the fully shared network. In addition, the limited binary supervised information (matching or nonmatching) is insufficient to train the deep matching networks for optical-SAR image registration. Thus, we propose a self-distillation feature learning method to exploit more similarity information for deep network optimization enhancement, such as the similarity ordering between a series of nonmatching patch pairs. The exploited rich similarity information will significantly enhance network training and improve matching accuracy. Finally, existing deep learning methods brute-force make the matching features of the optical and SAR image patches similar, which will lead to the loss of discriminative information and degeneration of the matching performances. Thus, we build an auxiliary task reconstruction learning to optimize the feature learning network to keep more discriminative information. Extensive experiments demonstrate the effectiveness of our proposed method on multimodal image registration. [ABSTRACT FROM AUTHOR]

Differential synthetic aperture radar interferometry (DInSAR) allows for retrieval of ice velocity measurements of high resolution and accuracy. One of the main error sources in DInSAR is the phase unwrapping procedure. Unwrapping errors may be caused by several processes, including shear stresses associated with large motion gradients, which lead to loss of interferometric coherence. In many cases, unwrapping errors reach magnitudes corresponding to velocities of tens or even hundreds of meters per year. Traditional DInSAR implementations include pixel masking based on coherence thresholding; however, such a masking is not always sufficient. Consequently, the state-of-the-art for ice velocity retrievals involves either manual inspection of individual measurements or simply discarding measurements in regions where ice flow exceeds a predefined threshold. Here, we instead apply a masking based on thresholding of a pixel connectivity estimate with respect to a reference point, which aims to detect unwrapping errors based only on the estimated coherence pattern. The method is tested on both simulated and real data Sentinel-1 data from the Greenland Ice Sheet and effectively detects the majority of unwrapping errors (recall of 0.84 for the best performing threshold), although with a relatively low precision (0.52 for the best performing threshold). Importantly, higher magnitude unwrapping errors are associated with lower connectivity values, meaning that undetected errors have a significantly lower magnitude (median of 1.7 m/y, corresponding to a single phase cycle, compared with 40.5 m/y with no masking). [ABSTRACT FROM AUTHOR]

In this article, we propose an efficient and generalizable framework based on a deep convolutional neural network (CNN) for multisource remote sensing (RS) data joint classification. While recent methods are mostly based on multistream architectures, we use group convolution (GConv) to construct equivalent network architectures efficiently within a single-stream network. Based on a recent technique called dynamic grouping convolution (DGConv), we further propose a network module named separable DGConv (SepDGConv), to make GConv hyperparameters, and, thus, the overall network architecture, learnable during network training. In the experiments, the proposed method is applied to residual network (ResNet) and UNet, and the adjusted networks are verified on three very diverse benchmark datasets (i.e., Houston2018 data, Berlin data, and MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set (MUUFL) data). Experimental results demonstrate the effectiveness of the proposed single-stream CNNs, and in particular, SepG-ResNet18 improves the state-of-the-art classification overall accuracy (OA) on hyperspectral–synthetic aperture radar (HS–SAR) Berlin dataset from 62.23% to 68.21%. In the experiments we have two interesting findings. First, using DGConv generally reduces test OA variance. Second, multistream is harmful to model performance if imposed to the first few layers, but becomes beneficial if applied to deeper layers. Altogether, the findings imply that the multistream architecture, instead of being a strictly necessary component in deep learning models for multisource RS data, essentially plays the role of model regularizer. Our code is publicly available at https://github.com/yyyyangyi/CNNs-for-Multi-Source-Remote-Sensing-Data-Fusion. We hope our work can inspire novel research in the future. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering images of the illuminated area, playing a significant role in various applications. However, achieving focused SAR images is challenging under sparse sampling and phase error conditions. By exploiting the sparsity or compressibility priors, the state-of-the-art sparsity-driven SAR imaging methods can reconstruct images under the condition of sparse sampling. However, the handcrafted priors used in these methods limit the imaging performance, and the iterative solution schemes reduce the computational efficiency. Besides, the measurement inaccuracy introduced by the phase error also degrades the reconstruction performance of the sparsity-driven imaging methods. To address these issues, a deep network for SAR autofocus imaging is proposed, which alternately performs image reconstruction and phase error estimation. When performing image reconstruction, the sparsity-cognizant total least-square (S-TLS) model is introduced to handle the problem of measurement inaccuracy, contributing to robust reconstruction performance under the condition of phase error. During the implementation of the deep network, a feature transform operator is used to realize data-driven prior knowledge learning and overcome the limitations of handcrafted priors. Moreover, the deep network approach can significantly improve computational efficiency. Experiments on simulated and real data verify the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Satellite remote sensing acquisitions are usually processed after downlink to a ground station. The satellite travel time to the ground station adds to the total latency, increasing the time until a user can obtain the processing results. Performing the processing and information extraction onboard of the satellite can significantly reduce this time. In this study, synthetic aperture radar (SAR) image formation as well as ship detection and extreme weather detection were implemented in a multiprocessor system on a chip (MPSoC). Processing steps with high computational complexity were ported to run on the programmable logic (PL), achieving significant speed-up by implementing a high degree of parallelization and pipelining as well as efficient memory accesses. Steps with lower complexity run on the processing system (PS), allowing for higher flexibility and reducing the need for resources in the PL. The achieved processing times for an area covering 375 km2 were approximately 4 s for image formation, 16 s for ship detection, and 31 s for extreme weather detection. These developments combined with new downlink concepts for low-rate information data streams show that the provision of satellite remote sensing results to end users in less than 5 min after acquisition is possible using an adequately equipped satellite. [ABSTRACT FROM AUTHOR]

Due to the significant acoustic impedance contrast at cortical boundaries, highly inside attenuation, and the unknown sound velocity distribution, accurate ultrasound cortical bone imaging remains a challenge, especially for the traditional pulse-echo modalities using unique sound velocity. Moreover, the large amounts of data recorded by multielement probe results in a relatively time-consuming reconstruction process. To overcome these limitations, this article proposed an index-rotated fast ultrasound imaging method based on predicted velocity model (IR-FUI-VP) for cortical cross section ultrasound tomography (UST) imaging, utilizing ray-tracing synthetic aperture (RTSA). In virtue of ring probe, the sound velocity model was predicted in advance using bent-ray inversion (BRI). With the predicted velocity model, index-rotated fast ultrasound imaging (IR-FUI) was further applied to image the cortical cross sections in the sectors corresponding to the dynamic apertures (DAs) and ring center. The final result was merged by all sector images. One cortical bone phantom and two ex vivo bovine femurs were utilized to demonstrate the performance of the proposed method. Compared to the conventional synthetic aperture (SA) imaging, the method can not only accurately image the outer cortical boundary but also precisely reconstruct the inner cortical surface. The mean relative errors of the predicted sound velocity in the region of interest (ROI) were all smaller than 7%, and the mean errors of cortical thickness are all less than 0.31 mm. The reconstructed images of bovine femurs were in good agreement with the reference images scanned by micro-computed tomography ($\mu $ CT) with respect to the morphology and thickness. The speed of IR-FUI is about 3.73 times faster than the traditional SA. It is proved that the proposed IR-FUI-VP-based UST is an effective way for fast and accurate cortical bone imaging. [ABSTRACT FROM AUTHOR]

A new approach is proposed to develop aperture-shared dual-band antennas using partially reflecting surfaces (PRSs). A fully aperture-shared dual-band antenna consisting of a low-band antenna (LBA) and a high-band antenna (HBA) right below the LBA is first investigated. The aperture sharing has little influence on the LBA but causes a severe shielding effect on the HBA. To alleviate the shielding effect, a low-pass PRS is inserted between the LBA and HBA, forming an air-filled quasi-Fabry–Perot cavity, which can make compensation for the radiation from the HBA but with little influence on the LBA. The second PRS is introduced above the LBA to enhance the bandwidth (BW) for HBA. It is shown that the fully aperture-shared dual-band antenna achieves an impedance BW ($\vert \text{S}11\vert < -15$ dB) of ~43.7% (0.68–1.07 GHz) for the LBA and ~45% (1.7–2.7 GHz) for the HBA with an average gain of ~8 dBi and stable radiation patterns. Based on the PRSs, an aperture-shared dual-band antenna subarray is finally developed for base-station applications, which covers the frequency bands of 0.68–0.96 and 1.7–2.7 GHz for 2G/3G/4G mobile communications. [ABSTRACT FROM AUTHOR]

A millimeter-wave (mm-wave) system architecture with enhanced-gain antenna solution is proposed for fifth-generation (5G) wireless communications. To enhance the antenna gain, the surface wave currents are converted to constructive far-field radiations through a novel two-stage metallic and dielectric rings. By applying low-profile dielectric ring, the electric near-field distributions of the grounded quarter-wavelength metallic ring are modified to create radiation apertures with appropriate polarization and orientation. The mutual coupling between transmit/receive antennas and cross polarizations is reduced by 7 and 5 dB, respectively, and the gain is enhanced over 4 dB. Low-loss mm-wave transitions are implemented in a standard FR-4 printed circuit board and in an interposer substrate to integrate the active antenna with the rest of the wireless system on a compact universal serial bus (USB)-interface platform. To evaluate the interposer package and define the output power of the embedded 60-GHz RF IC, an evaluation module with a standard WR-15 waveguide interface is designed and implemented. A prototype of the architecture, which is entirely realized using low-cost organic substrates, is presented and evaluated by experiments. Excellent correlations are achieved between simulations and measurements, demonstrating more than 25 dBm of equivalent isotropically radiated power (EIRP) over the unlicensed 60-GHz frequency band. [ABSTRACT FROM AUTHOR]

The polarimetric interferometric synthetic aperture radar (Pol-InSAR) model under P-band observations exhibits vertical structure diversity. Compared with the exponential-based random volume over ground (RVoG) model, the Gaussian vertical backscatter volume over ground (GVBVoG) model expresses a more complex forest vertical structure via introducing more parameters. On account of the influence of topographic fluctuation on the model, this article establishes the sloped Gaussian vertical backscatter volume over ground (SGVBVoG) model by drawing into the terrain slope. Based upon the SGVBVoG model, this article develops the 2-D SGVBVoG (2-D-SGVBVoG) model by defining the structure factor, which effectively reduces the model complexity from three to two dimensions. In the 2-D-SGVBVoG model inversion, in view of the diversity of forest species, age, shape, density, etc., in the natural scene and the variation of specific radar systems, a structure factor prediction scheme relying on machine learning is proposed. In the machine-learning model training, the radar incidence angle and the PDHigh coherence acquired by coherence optimization with terrain phase removal are utilized as the variables for characterizing the structure factor. Ultimately, in the case of fixing structure factor, a geometric inversion process on the complex plane is put forward to extract the forest height. The BIOSAR 2008 P-band Pol-InSAR data validation shows that the proposed method achieves an RMSE of 3.07 m, which is 24.0% better than the three-baseline SRVoG inversion. [ABSTRACT FROM AUTHOR]

Synthetic aperture sonar (SAS) systems produce high-resolution images of the seabed environment. Moreover, deep learning has demonstrated superior ability in finding robust features for automating imagery analysis. However, the success of deep learning is conditioned on having lots of labeled training data but obtaining generous pixel-level annotations of SAS imagery is often practically infeasible. This challenge has thus far limited the adoption of deep learning methods for SAS segmentation. Algorithms exist to segment SAS imagery in an unsupervised manner, but they lack the benefit of state-of-the-art learning methods and the results present significant room for improvement. In view of the above, we propose a new iterative algorithm for unsupervised SAS image segmentation combining superpixel formation, deep learning, and traditional clustering methods. We call our method iterative deep unsupervised segmentation (IDUS). IDUS is an unsupervised learning framework that can be divided into four main steps: 1) a deep network estimates class assignments; 2) low-level image features from the deep network are clustered into superpixels; 3) superpixels are clustered into class assignments (which we call pseudo-labels) using $k$ -means; and 4) resulting pseudo-labels are used for loss backpropagation of the deep network prediction. These four steps are performed iteratively until convergence. A comparison of IDUS to current state-of-the-art methods on a realistic benchmark dataset for SAS image segmentation demonstrates the benefits of our proposal even as the IDUS incurs a much lower computational burden during inference (actual labeling of a test image). Because our design combines merits of classical superpixel methods with deep learning, practically we demonstrate a very significant benefit in terms of reduced selection bias, i.e., IDUS shows markedly improved robustness against the choice of training images. Finally, we also develop a semi-supervised (SS) extension of IDUS called Iterative Deep SS Segmentation (IDSS) and demonstrate experimentally that it can further enhance performance while outperforming supervised alternatives that exploit the same labeled training imagery. [ABSTRACT FROM AUTHOR]

Arc-synthetic aperture radar (ArcSAR) can observe the surrounding ground objects at 360°, with the advantages of a wide imaging range and constant azimuth angular resolution. In this article, based on the stepped frequency continuous wave (SFCW), fourth-order Taylor expansion, and the series-inversion method, the accurate phase expression of the 2-D spectrum is derived, and then, the 2-D frequency-domain imaging algorithm (2-D-FDA) for ArcSAR is realized. In the simulation part, the imaging results of 2-D-FDA are compared with the backprojection algorithm (BPA) and Liao’s method, which is the reference for 2-D-FDA. The simulation results show that the 2-D-FDA is more applicable to process the data obtained from the antenna with the beamwidth larger than 70°. The SFCW-ArcSAR system consists of a vector network analyzer (VNA), a rotating platform, two antennas, and a control computer. Then, the system is used for the outdoor experiment. The experimental results of the corner reflector show that the point target analysis of 2-D-FDA is consistent with those of the BPA and theory. Under the system parameters in this article, the measured azimuth angular resolution is 0.0177 radian, the peak sidelobe ratio (PSLR) in the range direction is −12.46 dB, and the range resolution is 0.48 m on the ground-range plane. The imaging results of the real scenario processed by BPA and 2-D-FDA are consistent, and the ArcSAR image agrees with the optical image of the experimental scene. The effectiveness and correctness of the 2-D-FDA are verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

In complex electromagnetic environments, synthetic aperture radar (SAR) is severely affected by radio frequency interference (RFI) from other systems, such as ground-based radar, cellular networks, and global positioning systems, and this interference cannot be neglected. Pulse RFI (PRFI), a common form of RFI, can hinder SAR signal processing and image interpretation to varying degrees. The time-domain notch filtering method designed for mitigating PRFI can locate and mitigate the evident PRFI covered in SAR echo data, but it is helpless against PRFI hidden in a strong echo signal. In this article, a three-step approach is proposed to tackle the PRFI problem. In the proposed approach, the first step is to detect and locate PRFI; this is based on eigenvalue decomposition (EVD) and the short-time Fourier transform (STFT). The second step is to notch PRFI, and this is based on a time-domain notch filter. The third step is to recover the notched signal using a novel matrix completion strategy, which integrates with a robust low-rank matrix completion (LRMC) technique—i.e.,the singular value thresholding (SVT) algorithm—and a well-known Lagrange interpolation technique. Experimental results via simulated SAR data, Sentinel-1 level-0 raw data, and L-band airborne SAR raw data demonstrate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) has extensive application prospects, mainly including the acquisition of multidimensional scattering information, high-resolution and wide-width (HRWS) imaging, and moving target indication (MTI). Its echo separation is the most technical challenge, and so far, the confirmation for orthogonal waveform encoding MIMO-SAR by airborne experiments has not been reported in any literature. Here, an echo separation experiment based on the segmented phase code (SPC) waveforms and an airborne digital beamforming SAR (DBF-SAR) system is demonstrated for the first time. In the experiment, the SPC waveforms are cyclically transmitted within the adjacent pulse repetition intervals (PRIs) to simulate multiple transmitters, and the scattered echoes are received by the 16-channel antennas in elevation at the same time. In the postprocessing, the echo signals of continuous PRIs are added to obtain the mixed echoes, and a detailed echo separation method is adopted. In the method, the mixed echo signals from close arrival angles and far arrival angles are separated by the time shift and weighting, and by the bandpass filtering and DBF technique, respectively. Through the presented method, the mixed echoes of dual-transmit and 16-receive (2T16R) SAR imaging mode are separated and imaged successfully. The experimental results not only validate the echo separation scheme but also indicate that it is very promising in future MIMO-SAR missions. [ABSTRACT FROM AUTHOR]