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Radar-derived refractivity from stationary ground targets can be used as a proxy of near-surface moisture field and has the potential to improve the forecast of convection initiation. Refractivity retrieval was originally developed for a single radar and was recently extended for a network of radars by solving a constrained least squares (CLS) minimization. In practice, the number of high-quality ground returns can be often limited, and consequently, the retrieval problem becomes ill-conditioned. In this paper, an emerging technology of compressive sensing (CS) is proposed to estimate the refractivity field using a network of radars. It has been shown that CS can provide an optimal solution for the underdetermined inverse problem under certain conditions and has been applied to different fields such as magnetic resonance imaging, radar imaging, etc. In this paper, a CS framework is developed to solve the inversion. The feasibility of CS for refractivity retrieval using single and multiple radars is demonstrated using simulations, where the model refractivity fields were obtained from the Advanced Regional Prediction System. The root-mean-squared error was introduced to quantify the performance of the retrieval. The performance of CS was assessed statistically and compared to the CLS estimates for various amounts of measurement errors, numbers of radars, and model refractivity fields. Our preliminary results have shown that CS can consistently provide relatively robust and high-quality estimates of the refractivity field. [ABSTRACT FROM AUTHOR]

Motivated by classical coincidence imaging which has been realized in optical systems, an instantaneous microwave-radar imaging technique is proposed to obtain focused high-resolution images of targets without motion limitation. Such a radar coincidence imaging method resolves target scatterers based on measuring the independent waveforms of their echoes, which is quite different from conventional radar imaging techniques where target images are derived depending on time-delay and Doppler analysis. Due to the peculiar features of coincidence imaging, there are two potential advantages of the proposed imaging method over the conventional ones: 1) shortening the imaging time to even a pulse width without resolution deterioration so as to improve the performance of processing noncooperative targets and 2) simplifying the receiver complexity, resulting in a lower cost and platform flexibility in application. The basic principle of radar coincidence imaging is to employ the time-space independent detecting signals, which are produced by a multitransmitter configuration, to make scatterers located at different positions reflect independent waveforms from each other, and then to derive the target image based on the prior knowledge of this detecting signal spatial distribution. By constructing the mathematic model, the necessary conditions of the transmitting waveforms are analyzed for achieving radar coincidence imaging. A parameterized image-reconstruction algorithm is introduced to obtain high resolution for microwave radar systems. The effectiveness of this proposed imaging method is demonstrated via a set of simulations. Furthermore, the impacts of modeling error, noise, and waveform independence on the imaging performance are discussed in the experiments. [ABSTRACT FROM PUBLISHER]

Tomographic profiling (TP) is a new imaging technique designed to provide vertical backscatter profiles through biophysical and geophysical target volumes, such as snow, ice, vegetation, and forest canopies. Data is collected as for normal synthetic aperture radar (SAR) imaging, but with the antennas aligned along the scan or along-track direction. The real antenna provides a wide beam in the along-track direction, which is sharpened by the addition of elemental measurements across a subaperture using a SAR-like processing scheme. A novelty of the scheme is the ability to produce an image transect in which the incidence angle is constant at every point. This is accomplished by incrementally sliding the subaperture across the full aperture, and utilizing the appropriate subaperture to provide the necessary viewing geometry at each pixel. This is in contrast to the SAR case, in which the angle of incidence varies across a scene. By suitable phasing between the subaperture elements, the synthesized beam can be steered in angle within the wide angular extent of the real beam, allowing post-measurement retrieval of the backscattering properties of the scene over a continuous range of incidence angles from a single scan. In the across-track direction, a narrow real beam is required to maintain good vertical resolution and limit the size of the horizontal footprint. Example TP experimental fieldwork results are provided for a 42-cm-deep snowpack, collected with a ground-based SAR system. Although the scheme was developed for ground-based applications, its application to the airborne and satellite cases is also discussed. [ABSTRACT FROM AUTHOR]

This paper describes a new approach for forming change images from multistatic radar data based on compressive sensing (CS). Broadly speaking, change images are naturally sparse in the raw image domain, which suggests a CS reconstruction method. Recent results show that the sparsity of the estimand dictates the number of samples required for faithful reconstruction, meaning a change image can be formed with far fewer measurements than used for conventional radar imaging. Our application has a small number of antennas arranged around the perimeter of a surveillance region, which provide large angular diversity but very poor angular sampling. Furthermore, due to application constraints, the scene is interrogated with limited frequency diversity. We aim to construct a high-resolution change image from the measurements, which are sub-Nyquist both spatially and in frequency. This paper first develops a radar imaging model in the context of CS, and then shows with collected data that a sparseness model improves image utility over conventional methods in our setting. [ABSTRACT FROM AUTHOR]

The accurate estimation of internal solitary waves' (ISWs') amplitudes from radar images is important for understanding the ISW evolution, energy dissipation, and mixing processes. The in situ data from the Non-Linear Internal Wave Initiative experiment in the Mid-Atlantic Bight show many ISWs with amplitudes of 10 m or more in a shallow water depth of 80 m or less. Therefore, the higher order Korteweg–de Vries (KdV) equation in a two-layer system is needed to describe these large-amplitude ISWs instead of the classic KdV equation. Based on a simple theoretical radar imaging model, we develop a method to estimate large ISW amplitudes from distances between the positive and negative peaks of ISW signatures in radar images and a selection rule from the two possible amplitude solutions. Two groups of ISWs with large amplitudes, determined from the temperature records from nearby moorings, are observed in a RADARSAT synthetic-aperture-radar image and in marine X-band radar data collected during the experiment. We validate the method using the ISW signatures taken from these two cases. We find the estimated amplitudes to agree well with those determined from the moorings. The proposed method provides a relatively simple and accurate way to estimate large ISW amplitudes from radar images. [ABSTRACT FROM AUTHOR]

Operational weather radars in the U.S. and other countries in the world are challenged in providing low-altitude observations of rainfall due to the Earth's curvature and their deployment in “sparse” networks spaced hundreds of km apart. Given this limitation, work is underway to explore the feasibility of “dense” networks of small X-band radars. One approach developed by a student team from the U.S. Engineering Research Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) uses low-cost networks of simple, single-polarization radars that are not dependent on existing infrastructure, operating using solar energy and ad-hoc wireless networks, providing gap-filling data with improved temporal and spatial resolution. This “off-the-grid” (OTG) concept is one that might offer a means to monitor rainfall and provide useful data where it is not feasible or cost-effective to deploy more costly and more accurate radars. This paper describes the OTG concept and design, and presents examples of collected data and respective comparisons from this OTG network with measurements from an S-band NEXRAD radar as well as rainfall data from a set of rain gauges located in Puerto Rico. Results show that CASA OTG radars can provide improved spatial and temporal rainfall estimates with consistent or smaller estimated errors when compared to the S-band radar. End user validation was demonstrated in collaboration with the U.S. National Weather Service during system deployment for the XXI Central American and Caribbean Games celebrated at Mayaguez, Puerto Rico during the Summer of 2010. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method for the classification of synthetic aperture radar (SAR) images based on a novel feature vector. The method aims at combining the intensity information of pixels with spatial information and structural relationships. Unlike classical approaches which define a static neighborhood via a rectangular moving window of predefined size and relate spatial information for each center pixel to all the pixels within that window, the local primitives (LPs) proposed in this study provide us with an adaptive neighborhood so that spatial information for each center pixel is extracted only from the related pixels in its neighborhood. LPs correspond to local homogeneous connected components that describe the pixel neighborhood more consistently than the fixed size window approach. A feature vector, called as the LP pattern (LPP), is constructed for each pixel. The feature vector includes information about the sizes, intensity levels, and contrast differences of LPs within a disk whose center is the pixel under consideration as well as the repetitive frequency of LPs outside that disk. Finally, a kernel-based support vector machine is used with the proposed feature vectors for the classification of SAR images. Experimental analysis presents that the new feature extraction technique is well suited to depict spatial information and structural relationships and it yields promising results for the classification of SAR images when compared to common features such as gray-level co-occurrence matrix and Gabor coefficients. [ABSTRACT FROM PUBLISHER]

A contrast-based phase calibration algorithm for digital beamforming remote sensing radars using three contrast metrics is presented. The algorithm corrects time-varying antenna array phase errors that defocus digital beamforming remote sensing radar imagery. Amplitude errors are treated by equalizing the received powers in all elements. As such, the algorithm does not produce an absolute (or radiometric) calibration vector for the array. The performance of the algorithm is studied using a combination of simulated and real radar data under various conditions and is compared with a clutter-based calibration algorithm. An analytical proof showing that maximizing the expected value of the 4-norm metric is equivalent to phase-calibrating the image, except for a linear phase offset, is provided. We find that the clutter calibration algorithm performs best for statistically homogeneous scenes but that the contrast-calibration algorithms perform better with scenes with larger contrast ratios. [ABSTRACT FROM PUBLISHER]

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

Waveform-based tomographic imaging of crosshole georadar data is a powerful method to investigate the shallow subsurface because of its ability to provide images of electrical properties in near-surface environments with unprecedented spatial resolution. A critical issue with waveform inversion is the a priori unknown source signal. Indeed, the estimation of the source pulse is notoriously difficult but essential for the effective application of this method. Here, we explore the viability and robustness of a recently proposed deconvolution-based procedure to estimate the source pulse during waveform inversion of crosshole georadar data, where changes in wavelet shape with location as a result of varying near-field conditions and differences in antenna coupling may be significant. Specifically, we examine whether a single, average estimated source current function can adequately represent the pulses radiated at all transmitter locations during a crosshole georadar survey, or whether a separate source wavelet estimation should be performed for each transmitter gather. Tests with synthetic and field data indicate that remarkably good tomographic reconstructions can be obtained using a single estimated source pulse when moderate to strong variability exists in the true source signal with antenna location. Only in the case of very strong variability in the true source pulse are tomographic reconstructions clearly improved by estimating a different source wavelet for each transmitter location. [ABSTRACT FROM AUTHOR]

A scheme for target detection using segmentation by classification is proposed. The scheme is applied to through-the-wall microwave images obtained using frequency-domain back-projection in a wideband radar. We consider stationary targets where Doppler and change-detection-based techniques are inapplicable. The proposed scheme uses features from polarimetric images to segment and classify the image observations into target, clutter, and noise segments. We map target polarization signatures from copolarized and cross-polarized target returns to a pixel-by-pixel feature space, then oversegment the image to homogeneous regions called superpixels depending on this feature space. The features of each superpixel are used subsequently to group homogeneous superpixels into clusters. The clusters are then classified using decision trees. Real data collected using an indoor radar imaging scanner are used for performance validation. [ABSTRACT FROM PUBLISHER]

Radar imaging of deserts suffers from insufficient radar backscatter at low microwave frequencies due to the low permittivity of dry sand and relatively smooth sand surface roughness. Operating at millimeter-wave (MMW) frequencies, however, rectifies this deficiency as significant radar backscatter is generated by surface and volume scattering. This is due to the fact that sand surface roughness is electrically large and signal penetration into the dry sand, which is a homogeneous mixture of air and sand particles with dimensions comparable to a fraction of a wavelength, generates considerable volume scattering. This paper investigates both surface and volume scattering from dry sand surfaces, subject to the peculiar physical properties of sand surfaces found in sand dune-covered regions. An incoherent model is proposed that characterizes the angular dependence of volume scattering from dry sand in the presence of a 1-D rippled air/sand surface. A set of indoor experiments conducted on smooth and 1-D rippled sand surfaces at Ka-band confirms that significant volume scattering is present at MMW frequencies and that the proposed model correctly captures the observed angular dependence when 1-D surface ripples are present. [ABSTRACT FROM AUTHOR]

We consider the problem of through-the-wall radar imaging (TWRI), in which polarimetric imaging is used for automatic target detection. Two generalized statistical detectors are proposed which perform joint detection and fusion of a set of multipolarization radar images. The first detector is an extension of a previously proposed iterative target detector for multiview TWRI. This extension allows the detector to automatically adapt to statistics that may vary, depending on target locations and electromagnetic-wave polarizations. The second detector is based on Bayes' test and is of interest when target pixel occupancies are known from, e.g., secondary data. Properties of the proposed detectors are delineated and demonstrated by real data measurements using wideband sum-and-delay beamforming, acquired in a semicontrolled lab environment. We examine the performance of the proposed detectors when imaging both metal objects and humans. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for high-resolution imaging of group targets moving in a formation with constant accelerated rectilinear motion. In this method, the first- and second-order phase terms are compensated simultaneously to obtain a “bulk” image of group targets. Then, regions of subtargets are determined by the segmentation method based on clustering number estimation and normalized cuts. Finally, refined compensation of the second- and third-order phase terms is carried out jointly to obtain a well-focused image of group targets, following the minimum local image entropy criterion. Simulation results have proved the validity of the proposed method. [ABSTRACT FROM AUTHOR]

The most intuitive way to extract depth information from remote sensing images is stereogrammetry, in which a digital elevation model (DEM) is achieved by computing stereoscopic radar images. When only the amplitude of the radar images is considered, this computation is called radargrammetry. The main idea of which is to match stereopair radar images in order to create a disparity map from one image to the other and, finally, to compute the elevation. Therein, we present our studies on the extraction of 3-D information from radar images. We examine a way to produce a DEM of a challenging area of the French Alps. The central issue of this paper concerns improvements for radargrammetric synthetic aperture radar image processing for high-relief reconstruction, and we focus on the matching step, which is one of the most important points of the radargrammetric processing. Thus, we propose original methods using different correlation windows. On the one hand, we take the advantages of a multiwindow approach to combine relevant information by multiplying the correlation surfaces obtained for each correlation window size during the matching operation. On the other hand, the second improvement is based on the expansion of windows on foreshortened areas, particularly because of the side-looking radar view. These methods allow us to achieve reliable image matching and to improve the accuracy of the DEM. [ABSTRACT FROM AUTHOR]

We develop and then demonstrate a 3-D tomographic ice sounding method applied to very high frequency (VHF) radar data that produces swath measurements of ice sheet surface topography, ice thickness, and radar reflectivity of both the surface and bed of the ice sheet. First, we formulate the ice sheet imaging problem as a problem of estimating signal arrival angles and illustrate how the method resolves ambiguous echoes arriving simultaneously from the left and right sides of the aircraft, as well as from the surface and base of the ice sheet. We then discuss why we chose the time-domain subaperture method for 2-D image formation for ice sounding. We apply the tomographic technique to the data that we collected in May 2006 and again in July 2008 from a multiple-phase-center VHF radar system. We present 3-D images of the upper and lower surfaces of the ice sheet and compare the estimated surface topography with Ice, Cloud, and land Elevation Satellite altimeter nadir track measurements and the measured swath ice thickness with independent nadir depth sounder tracks. We achieved a 5-m surface topography accuracy and a 14-m ice thickness accuracy. [ABSTRACT FROM AUTHOR]

Conventional inverse synthetic aperture radar image is a 2-D range-Doppler projection of a target and does not provide 3-D information. Its formation also requires complex motion compensation when the target is uncooperative and maneuvering. On the other hand, multiple-input and multiple-output (MIMO) radar, in addition to having a wide virtual aperture and high cross-range resolution, could also obtain a target's 3-D image in one snapshot and thus have avoided the complex motion compensation needed. In this paper, we propose a 3-D imaging algorithm using three MIMO configurations. The signal model is derived based on a modified zero correlation zone code. A strong scatterer selection criterion is also proposed for the construction of the target profile. [ABSTRACT FROM PUBLISHER]

Fast rotating targets such as gimbaled antennas or propeller blades may cause migration through resolution cells (MTRC) of the high-resolution range profile during the imaging time, which makes the inverse synthetic aperture radar image smeared. To solve this problem, a novel imaging method for fast rotating targets is proposed in this paper. The method is based on the segmental pseudo Keystone transform, which is designed to realize MTRC correction. The fast realization is achieved by employing the discrete match Fourier transform, which makes the algorithm feasible and simple. Experiments with simulated radar data demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a model for radar images of fractal (topologically 1-D) profiles is introduced. A twofold approach is followed: on one hand, we analytically solve the problem whenever small-slope profiles are in order; on the other hand, we present a partly analytical and partly numerical setup to cope with the general-slope case. By means of the analytical approach, we evaluate in closed form both the structure function and the power density spectrum of the radar signal. An appropriately smoothed (physical) fractional Brownian model (fBm) process is employed; its introduction is justified by the finite sensor resolution. A fractal scattering model is employed. It is shown that for a fractal profile modeled as an fBm stochastic process, the backscattered signal turns out to be strictly related to the associated fractional Gaussian noise process if a small-slope regime for the observed profile can be assumed. In the analytical--numerical framework, a profile with prescribed fractal parameters is first synthesized; then, fractal scattering methods (applicable to wider slope regimes with respect to the previous case) are employed to compute the signal backscattered toward the sensor. Finally, the power density spectrum of the acquired radar image is estimated. The obtained spectra are favorably compared with the theoretical results, and a parametric study is performed to assess the overall method behavior. [ABSTRACT FROM AUTHOR]

A high-frequency asymptotic technique based on the Uniform Geometric Theory of Diffraction (UTD) is employed for building interior imaging. The analysis is implemented using a ray-tracing technique to account for multiple scattering interactions in a building, along with a set of heuristic diffraction coefficients for dielectric wedges and corners. Imaging of the synthetic aperture radar data is carried out by the conventional fast Fourier transform method to transform to the downrange domain, and combined with a coherent near-zone imaging function for cross-range. Comparisons with experimental data for a scaled-down building model are given to demonstrate the suitability and efficacy of our analysis for through-wall building imaging. The UTD ray mechanisms account for the dominant scattering features observed in the image. [ABSTRACT FROM AUTHOR]

A detector of targets behind walls and in enclosed structures is presented. The detector is applied to through-the-wall radar images obtained by wideband delay and sum beamforming. We consider the detection problem using single- and multiple-view imaging. The statistics of noise, clutter, and target images are examined and formulated using sample scenes. The effects of wall parameter errors on the image statistics are shown. An iterative detection scheme, which adapts itself to the image statistics, is presented. The proposed detection schemes are evaluated using real data. [ABSTRACT FROM AUTHOR]

In this paper, a high-resolution P-band Pol-InSAR data set acquired by the airborne RAMSES system over pine forest stands of different height is investigated. A significant penetration depth in all the polarimetric channels and a wide range of polarimetric phase center heights are observed, attesting of an interaction of the radar waves with different forest structural elements. The main objective of this paper concerns forest height inversion at P-band. First, forest-modeling assumptions are evaluated using a priori information, such as ground-level and forest height measurements. The full extend of the forest height is shown to be responsible of the volume decorrelation, and a significant orientation effect is clearly identified over the highest stands. As a consequence, the Oriented Volume over Ground model (OVoG) is determined to be the most appropriated model for the forest height inversion. At P-band, the ground contribution is present in all the polarimetric channels due to the important penetration at this frequency. To overcome this difficulty, a time-frequency optimization method based on sublook decomposition is developed to separate the pure ground and canopy contributions, allowing forest height estimation with OVoG with an rms error on the order of 2 m. In the last section of this paper, a sensitivity analysis of the inversion with respect to two important system parameters, the signal-to-noise ratio and the resolution, is presented, leading to a discussion on the inversion robustness in spaceborne conditions, where these system parameters are the most deteriorated as compared to airborne configurations. [ABSTRACT FROM AUTHOR]