Elevation Dimension Research Articles

A weather signal detection algorithm based on EVD in elevation for airborne weather radar

Interest is growing in the application of the spatial location information from weather scenario to the monitoring of meteorological signal for airborne weather radar system. Here, a new algorithm utilizes the differences between ground clutter and meteorological signal in terms of the spatial location in elevation dimension to achieve weather signal detection (WSD). Specifically, weather signal submerged in ground clutter background is detected over a so-called diagram of the second eigenvalue to improve weather observation. Generally, this diagram is acquired by implementing eigenvalue decomposition (EVD) operation on raw data which is collected via dual-channel in elevation. For WSD, the second eigenvalue in the diagram is employed as the test statistic. And this study investigates the statistics of the second eigenvalue in the case that weather signal component, ground clutter component and Gaussian noise are all complex Gaussian distributed and they are statistically independent. Based on the statistics, a constant false-alarm rate (CFAR) detector is also designed and then the second eigenvalue of the cell under test undergoes the CFAR detector to screen out the pixels containing meteorological signal component. Simulations, as well as experimental results, are presented to demonstrate the theoretical analysis and to evaluate the detection performance of the proposed EVD-based algorithm. As compared to most of the current WSD approaches, the presented EVD-based method really shows increased detection capability and great robustness.

Meaning-centered group psychotherapy in Portuguese cancer patients: A pilot exploratory trial.

To describe the feasibility of a meaning-centered group psychotherapy (MCGP) adaptation in a sample of Portuguese cancer patients. The study was carried out according to four steps: 1st - Transcultural adaptation and validation (focus groups); 2nd - Preliminary study with MCGP original version (to test its feasibility); 3rd - Adaptation of MCGP original version to a 4-session version (and internal pilot study); and 4th - Pilot exploratory trial (MCGP-4 session version), implemented between January 1, 2018 and December 31, 2019. Inclusion criteria were >18 years, psychological complaints, and difficulty to adapt to cancer. Allocation was according to participants' preference: MCGP vs. care as usual (CAU). Primary outcomes were: MCGP adapted version improved quality of life (QoL) and spiritual well-being; secondary outcomes were improvement of depression, anxiety, and distress. Assessments were done at baseline (T1) and 1 month after (T2), with self-report socio-demographic and clinical questionnaires, Distress Thermometer (DT), McGill Quality of Life Questionnaire (MQOL), Functional Assessment of Chronic Illness Therapy - Spiritual Well-Being Scale (FACIT-Sp-12), Hospital Anxiety and Depression Scale, and its subscales (HADS - HADS-D, HADS-A). In the 1st step, and through focus groups, the manual was reformulated and tested. The preliminary study (2nd step) with MCGP original version showed a high number of dropouts which could jeopardize the study and, after reframing the sessions content, MCGP was adapted to a 4-session version, and its feasibility was tested by an internal pilot study (3rd step). The pilot exploratory trial (4th step) had 91 participants. Most socio-demographic and clinical characteristics between the groups (51: MCGP; 40: CAU) had no statistically significant differences. A comparison between the two groups at T2 showed that the MCGP group scored significantly higher in the general (U = 552.00, P < 0.001), and existential (U = 727.50, P = 0.018) domains and total score (U = 717.50, P = 0.015) of QoL, and CAU presented statistical higher levels in DT (U = 608.50, P = 0.001). Comparing the groups between T1 and T2, the MCGP group had a statistically significant improvement in the general (Z = -3.67, P < 0.001) and psychosocial (Z = -2.89, P = 0.004) domains and total score (Z = -2.71, P = 0.007) of QoL, and a statistically significant decrease in DT (Z = -2.40, P = 0.016). In terms of group effects, the MCGP group presented increased general (b = 1.42, P < 0.001, η2p = 0.179), and support (b = 0.80, P = 0.045, η2p = 0.048) domains and total score (b = 0.81, P = 0.013, η2p = 0.073) of QoL (small to elevated dimensions), and decreased levels of depression (b = -1.14, P = 0.044, η2p = 0.048), and distress (b = -1.38, P = 0.001, η2p = 0.127) (small to medium dimensions), compared with CAU. At T2, participants who attended ≥3 sessions (n = 38) had a statistically significant higher score in the general domain (U = 130.50, P = 0.009) of QoL, comparing with those who attended 1 or 2 sessions (n = 13). This study supports the benefits of an MCGP adapted version in improving QoL and psychologic well-being. More studies are necessary to address the limitations of this pilot exploratory trial, as its small sample size.

A New 3-D Imaging Technique Integrating Ultrafast Compounding, Hadamard Encoding, and Reconfigurable Fresnel Lensing

Crossed electrode arrays address some of the challenges associated with 3-D ultrasound imaging because of the significant reduction in the number of elements ( 2N versus N2 ). However, creating a two-way focused 3-D image in real time is difficult with these arrays because azimuth and elevation dimensions cannot be beamformed at the same time. This work describes a new 3-D imaging technique that uses the flexibility of bias-sensitive substrates to create a high-quality elevation focus on a crossed electrode array. The principle behind this technique is to perform conventional compound imaging with an azimuth set of electrodes while implementing a bias controllable elevation lens with an elevation set of electrodes. On transmit, the biases are chosen to mimic a Fresnel lens. Then, on receive, the Hadamard coding is implemented along the elevation dimension. After decoding, we gain the RF data for each element across the elevation aperture even though there is effectively only one channel in that dimension. A 30-MHz, 128-element crossed electrode relaxor array was fabricated on a 1-3 electrostrictive composite substrate and was used to demonstrate the performance of the imaging technique. The on-axis -6-dB beamwidths were simulated to be 175 and [Formula: see text] in the azimuth and elevation directions, respectively, and the focus remained isotropic in the furthest elevation slice. Images were generated of a wire phantom to confirm the performance of the azimuth and elevational radiation patterns with good agreement between simulation and experiment. High-resolution 3-D volumetric images were generated of an ex vivo rat brain. Images of the cerebellum showed that the white and gray matter tracts could clearly be visualized with isometric resolution in both the azimuth and elevation dimensions.

A Sequential ESPRIT Algorithm Based on a Novel UCSA Configuration for Parametric Estimation of Two-Dimensional Incoherently Distributed Source

Two-dimensional distributed source is a more realistic model to represent the target signal propagation in such fields as radar, sonar, and wireless communication. This paper presents a novel uniform circular sub-array (UCSA) configuration, which can be used to accurately estimate parameters of multiple two-dimensional incoherently distributed (TDID) sources. By sequentially utilizing the mapping relationship between the generalized array manifold, which has the spatial translation invariant relationship, and the signal subspace of TDID source, a closed-form solution of four parameters (central and spread parameters within azimuth and elevation dimension) of every TDID source can be obtained with low computational complexity. Furthermore, by properly using the signal subspace, the estimation accuracy is improved significantly. Numerical simulations illustrate the properties of the proposed method and show that it can simultaneously improve the robustness with respect to the scattering size and uncertainty of the spatial distribution. The proposed method has the potential to be used for fast and accurate multi-TDID source localization with arrays of small aperture and few array elements.

Parameter Estimation for Two-Dimensional Incoherently Distributed Source with Double Cross Arrays.

A direction of arrival (DOA) estimator for two-dimensional (2D) incoherently distributed (ID) sources is presented under proposed double cross arrays, satisfying both the small interval of parallel linear arrays and the aperture equalization in the elevation and azimuth dimensions. First, by virtue of a first-order Taylor expansion for array manifold vectors of parallel linear arrays, the received signal of arrays can be reconstructed by the products of generalized manifold matrices and extended signal vectors. Then, the rotating invariant relations concerning the nominal elevation and azimuth are derived. According to the rotating invariant relationships, the rotating operators are obtained through the subspace of the covariance matrix of the received vectors. Last, the angle matching approach and angular spreads are explored based on the Capon principle. The proposed method for estimating the DOA of 2D ID sources does not require a spectral search and prior knowledge of the angular power density function. The proposed DOA estimation has a significant advantage in terms of computational cost. Investigating the influence of experimental conditions and angular spreads on estimation, numerical simulations are carried out to validate the effectiveness of the proposed method. The experimental results show that the algorithm proposed in this paper has advantages in terms of estimation accuracy, with a similar number of sensors and the same experimental conditions when compared with existing methods, and that it shows a robustness in cases of model mismatch.

Persistent homology on LiDAR data to detect landslides

Landslides can result in loss of lives, cause damage to property, infrastructure, utilities, and residential structures and can block transportation routes. Landslide inventory maps can provide spatial-temporal information about past and recent landslides and are used for analysis to create models that can characterize susceptibility. As such, these maps are considered an essential source for risk management tasks. In this paper, we propose a persistent homology method applied on LiDAR-derived digital terrain model data to detect landslides for landslide inventory maps. In testing the hypothesis that persistent homology, a method for computing topological features of a space at different resolutions, can be used to accurately detect landslides, we applied the method on LiDAR-derived digital terrain models to detect shapes and patterns that are indicative of landslide surface expressions. We validated our test results by comparing them to currently available landslide inventory maps for selected locations in Pennsylvania, Oregon, Colorado and Washington. The results show a different performance for each state; the accuraces were 0.79, 0.71, 0.53, 0.69, and 0.77 for five study areas. Variations in performance are linked to varying surface roughness between different types of landslides, their size, shape, ages, composition, and a possible history of reactivations of landslides that would further pronounce surface expressions. To overcome some of these challenges that may hinder performance of our method, we recommend that other datasets, containing landslides, be considered as additional nth-order dimensions beyond the spatial and elevation dimensions inherent in topographic datasets.

Reduced-DOF Three-Dimensional STAP via Subarray Synthesis for Nonsidelooking Planar Array Airborne Radar

Compared with conventional two-dimensional space-time adaptive processing (2D-STAP) methods, the elevation-azimuth-Doppler three-dimensional space-time adaptive processing (3D-STAP) method has the advantage of suppressing nonstationary clutter. Thereby, it is suitable for nonsidelooking airborne radar (non-SLAR) applications. However, its huge training data requirements and computational load are often beyond radar's ability in practical clutter environments. In this correspondence, we develop a simple but efficient reduced-degree-of-freedom (DOF) 3D-STAP method that significantly reduces the required training data and the computational complexity while maintaining the suboptimal clutter suppression performance. The proposed method transforms the planar array data into linear array data in azimuth and in elevation, respectively, thereby beamforming an equivalent cross-shape array prior to STAP. In consequence, only a few spatial DOFs, including azimuth and elevation dimension, are used for STAP directly resulting in the potential advantage for nonstationary clutter suppression and drastically reducing training data requirements and computational load. Furthermore, the clutter rank estimation rules of the planar array and the transformed cross-shape array are derived, and the required elevation DOFs of the proposed method are further discussed in detail. Simulations for clutter suppression of non-SLAR show that the proposed STAP method outperforms the state-of-the-art 3D-STAP method in terms of convergence and computational complexity.

The Impact of Elevation Sidelobe Contamination on Radar Data Quality for Operational Interpretation

AbstractTo fulfill the evolving observational needs of the National Weather Service (NWS), future weather radar systems will have to meet demanding requirements. Designing such systems will likely involve trade-offs between system cost and operational performance. A potential cost driver for future weather radars that could cause significant data-quality impacts on forecasters is the required angular resolution and sidelobe performance, which are mainly dictated by the antenna radiation pattern. Typical antenna radiation patterns can be characterized by the width of the main lobe and their sidelobe levels, which are traditionally measured across the azimuthal and elevation dimensions. In this work, we study the impact of increasing sidelobe levels on NWS forecasters’ data interpretation during warning operations. The resulting impact model can be used by decision-makers to better understand the cost–benefit trade-offs inherent in any radar system design.

Quantitative sub-resolution blood velocity estimation using ultrasound localization microscopy ex-vivo and in-vivo

Super-resolution ultrasound imaging relies on the sub-wavelength localization of microbubble contrast agents. By tracking individual microbubbles, the velocity and flow within microvessels can be estimated. It has been shown that the average flow velocity, within a microvessel ranging from tens to hundreds of microns in diameter, can be measured. However, a 2D super-resolution image can only localize bubbles with sub-wavelength resolution in the imaging plane whereas the resolution in the elevation plane is limited by conventional beamwidth physics. Since ultrasound imaging integrates echoes over the elevation dimension, velocity estimates at a single location in the imaging plane include information throughout the imaging slice thickness. This slice thickness is typically a few orders or magnitude larger than the super-resolution limit. It is shown here that in order to estimate the velocity, a spatial integration over the elevation direction must be considered. This operation yields a multiplicative correction factor that compensates for the elevation integration. A correlation-based velocity estimation technique is then presented. Calibrated microtube phantom experiments are used to validate the proposed velocity estimation method and the proposed elevation integration correction factor. It is shown that velocity measurements are in excellent agreement with theoretical predictions within the considered range of flow rates (10 to 90 μl/min) in a microtube with a diameter of 200 μm. Then, the proposed technique is applied to two in-vivo mouse tail experiments imaged with a low frequency human clinical transducer (ATL L7-4) with human clinical concentrations of microbubbles. In the first experiment, a vein was visible with a diameter of 140 μm and a peak flow velocity of 0.8 mm s−1. In the second experiment, a vein was observed in the super-resolved image with a diameter of 120 μm and with maximum local velocity of ≈4.4 mm s−1. It is shown that the parabolic flow profiles within these micro-vessels are resolvable.

Elevated effective dimension in tree-like nanomagnetic Cayley structures.

Using state-of-the-art electron-beam lithography, Ising-type nanomagnets may be defined onto nearly any two-dimensional pattern imaginable. The ability to directly observe magnetic configurations achieved in such artificial spin systems makes them a perfect playground for the realization of artificial spin glasses. However, no experimental realization of a finite-temperature artificial spin glass has been achieved so far. Here, we aim to get a significant step closer in achieving that goal by introducing an artificial spin system with random interactions and increased effective dimension: dipolar Cayley tree. Through synchrotron-based photoemission electron microscopy, we show that an improved balance of ferro- and antiferromagnetic ordering can be achieved in this type of system. This combined with an effective dimension as high as d = 2.72 suggests that future systems generated out of these building blocks can host finite temperature spin glass phases, allowing for real-time observation of glassy dynamics.

Tensor Representation for Three-Dimensional Radar Target Imaging With Sparsely Sampled Data

Three-dimensional (3-D) radar imaging can provide additional information along elevation dimension about the target with respect to the conventional 2-D radar imaging, but usually requires a huge amount of data collected over 3-D frequency-azimuth-elevation space, which motivates us to perform 3-D imaging by using sparsely sampled data. Traditional compressive sensing (CS) based 3-D imaging methods with sparse data convert the 3-D data into a long vector, and then complete the sensing and recovery steps. This 1-D vectorized model, however, faces challenges of high computational complexity and huge memory usage and may not be viable in real applications. In this article, we solve the 3-D sparse imaging problem efficiently in a tensor way. For this aim, we firstly derive the 3-D imaging model from a tensor perspective under some assumptions. Then we review three kinds of sparse data sampling schemes that are common on the existing 3-D compressive radar imaging applications. Subsequently, with the help of prior information hidden in the radar signal, i.e., sparsity and low-rank property, we propose efficient image reconstruction algorithms for different sampling schemes to produce 3-D images with sidelobes and artifacts suppressed significantly. Finally, extensive experiments based on simulated and real-measured datasets are carried out. Results show that the proposed methods can effectively generate competitive images with small reconstruction error even when the data sampling ratio is low, which confirm the validity of proposed methods.

On 3D Cluster-Based Channel Modeling for Large-Scale Array Communications

With the rapid development of wireless communications, the understanding of three-dimensional (3D) propagation channels becomes essential for design and testing of some new wireless technologies, e.g., massive multiple-input multiple-output (MIMO) and full dimensional beamforming. To not only fully exploit the 3D multiplexing but also circumvent the size limitation of base station (BS), antenna elements in massive MIMO are usually arranged both horizontally and vertically. Based on an elaborate channel measurement campaign conducted at 11 GHz in a lobby environment, a 3D extended cluster-based channel model is proposed in this paper for massive multiple-input single-output (MISO) multi-user communications. In the model, the channel characteristics in both azimuth and elevation dimensions, and the visibility regions which are parametrized by observed cluster lengths across the large-scale array in both horizontal and vertical directions, are taken into consideration. Moreover, the spherical wavefront phenomenon observed from the measurements is also incorporated in the model. Model parametrization, implementation, and validation are presented in detail. Validations show that the proposed model can accurately reflect the realistic channel, and the spatial non-stationarity and the spherical wavefront should be carefully considered in the channel models for large-scale array communications.

Earthquake-induced sloshing effects on the hydrodynamic pressure response of rigid cylindrical liquid storage tanks using CFD simulation

This study aims at evaluating nonlinear hydrodynamic pressures on the inner wall of rigid cylindrical tanks, resulting from liquid sloshing due to the simultaneous action of two horizontal and one vertical ground motion components. For this purpose, the computational model that can predict realistic dynamic fluid responses is validated through comparison of simulated and experimental results reported in the literature. Using the validated computational fluid dynamics modeling technique, the computational model of a prototype cylindrical steel water tank constructed in California is developed to examine the effect of vertical ground motion component on the hydrodynamic pressure response of the tank. Comparison with and without vertical excitation indicates that the vertical motion significantly increases the hydrodynamic pressure on the inner wall of the tanks. Additionally, the effect of water elevation and tank dimensions (height and diameter) on the hydrodynamic pressure is investigated when the tanks are simultaneously subjected to two horizontal and one vertical ground motion components. As the water level and tank dimensions increase, the peak hydrodynamic pressure increases due to the increase of liquid mass.

Echo-Imaging Exploits an Environmental High-Pass Filter to Access Spatial Information with a Non-Spatial Sensor

SummaryEcho-imaging evolved as the main remote sense under lightless conditions. It is most precise in the third dimension (depth) rather than in the visually dominating dimensions of azimuth and elevation. We asked how the auditory system accesses spatial information in the dimensions of azimuth and elevation with a sensory apparatus that is fundamentally different from vision. We quantified echo-acoustic parameters of surface-wave patterns with impulse-response recordings and quantified bats' perceptual sensitivity to such patterns with formal psychophysics. We demonstrate that the spectro-temporal auditory representation of a wave pattern implicitly encodes its spatial frequency. We further show that bats are much more sensitive to wave patterns of high spatial frequencies than of low spatial frequencies. We conclude that echo-imaging accesses spatial information by exploiting an inherent environmental high-pass filter for spatial frequency. The functional similarities yet mechanistic differences between visual and auditory system signify convergent evolution of spatial-information processing.

Implementation and Analysis of 3D Channel Emulation Method in Multi-Probe Anechoic Chamber Setups

The multi-probe anechoic chamber (MPAC) based method is a candidate to assess the performance of multiple input multiple output (MIMO) devices over the air. This paper investigates the core problem, channel emulation method (CEM), in MPAC setups. The CEM is utilized to map the desired channel onto the limited number of probes to synthesize the target channel environment within a certain test area. In addition, the channel in realistic propagation environment is a three-dimensional (3D) channel, which can achieve higher data transmission rate by containing both azimuth and elevation dimension. Hence, it is urgent to investigate the CEM for 3D channels in MPAC setups. In this paper, the implementation of a novel CEM for 3D channels is presented, which allocates the subpaths in a simple way and complements the drawbacks of prefaded synthesis method and spatial fading emulator. Furthermore, the impact of the target channel and probe configuration on the emulation accuracy in terms of temporal correlation function (TCF) and spatial correlation function (SCF) is discussed. Simulation results demonstrate that the emulation accuracy of TCF depends more on the motion direction of MIMO devices while that of SCF depends more on the probes distribution. Moreover, probes number decides the emulation accuracy first. If the same number of probes are applied, the coincidence of the probe configuration and the target channel determines the emulation accuracy.

On the performance of three‐dimensionalantenna arrays in millimetre wave propagation environments

In order to reap the full scale of benefits of millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the design of antenna arrays at the transmitter or receiver becomes more critical due to the propagation characteristic at mm-frequencies. In this study, the authors investigate the performance of two types of antenna array, namely uniform rectangular planar array (URPA) and uniform cylindrical array (UCYA). The channel behaviour is presented in full-dimensional mmWave propagation conditions by considering both the azimuth and elevation dimensions. The squared inner product and singular value spread are studied for URPA and UCYA configurations, these properties reveal the effective interference and channel's stability of antenna array. The authors also evaluate the achievable rate with the equal power allocation and water-pouring power allocation schemes. Simulation results show that under the same system configurations, the performance that includes the radiation pattern, the channel eigenvalue distribution, the effective interference, and the achievable rate, of UCYA configuration always outperforms that of URPA configuration. Therefore, it can be concluded that in three-dimensional propagation environments, the UCYA configuration is especially appealing for mmWave MIMO systems.

Simultaneous Azimuth and Fresnel Elevation Compounding: A Fast 3-D Imaging Technique for Crossed-Electrode Arrays

We have developed a new, fast, and simple 3-D imaging approach referred to as Simultaneous Azimuth and Fresnel Elevation (SAFE) compounding using a bias-sensitive crossed-electrode array. The principle behind this technique is to perform conventional plane-wave compounding with a back set of electrodes, while implementing a reconfigurable Fresnel elevation lens with an orthogonal set of front electrodes. While a Fresnel lens would usually result in unacceptable secondary lobe levels, these lobes can be suppressed by compounding different Fresnel patterns. The azimuthal and elevational planes can be simultaneously compounded to increase the beam quality with no loss in frame rate. A 10-MHz, $64 \times 64$ element crossed-electrode relaxor array was fabricated on an electrostrictive one-to-three composite substrate to demonstrate the SAFE compounding approach. The electrostrictive composite array has a measured electromechanical coupling coefficient ( $k_{t}$ ) of 0.62 with a bias voltage of 90 V and a measured two-way pulse bandwidth of 60%. The electrical impedance magnitude of array elements on resonance was measured to be $90~\Omega$ with a phase angle of -35°. Radiation patterns were simulated showing a -6-dB beamwidth of $330~\mu \text{m}$ with secondary lobe levels suppressed more than -60 dB in the azimuth dimension, and a -6-dB beamwidth of $450~\mu \text{m}$ with secondary lobe levels suppressed to -50 dB in the elevation dimension after 64 compounds. Experimental radiation patterns were collected and found to be in good agreement with simulations. Experimental 3-D images of wire phantoms were collected using a Verasonics experimental ultrasound system.

Multichannel Staggered SAR Azimuth Processing

State-of-the-art and future spaceborne synthetic aperture radar (SAR) systems increasingly often face the requirement of providing high-resolution images with reduced revisit times, requiring coverage of wide swaths. Since these are contradicting drivers in terms of system design, different alternatives for high-resolution wide-swath SAR imaging have been investigated, relying on digital beamforming and the use of multiple receiver channels, both in elevation and azimuth dimensions. In this context, staggered SAR, which operates with a pulse repetition frequency (PRF) variation, using a single channel in azimuth proves itself as a promising alternative for covering wide continuous swaths with moderate azimuth resolution, whereas the use of multiple azimuth receiver channels bears the potential of improving the azimuth resolution over a given swath, but has yet only been applied to systems with a fixed PRF. This paper introduces and analyzes in detail processing techniques suitable for the combination of these techniques, leading to novel multichannel staggered SAR imaging modes with the potential for very fine azimuth resolution over ultra-wide swaths. A system concept with 2-m azimuth resolution over a 400-km swath in quad-pol is provided as an example.

Fast Fourier-based deconvolution for three-dimensional acoustic source identification with solid spherical arrays

Abstract Being capable of demystifying the acoustic source identification result fast, Fourier-based deconvolution has been studied and applied widely for the delay and sum (DAS) beamforming with two-dimensional (2D) planar arrays. It is, however so far, still blank in the context of spherical harmonics beamforming (SHB) with three-dimensional (3D) solid spherical arrays. This paper is motivated to settle this problem. Firstly, for the purpose of determining the effective identification region, the premise of deconvolution, a shift-invariant point spread function (PSF), is analyzed with simulations. To make the premise be satisfied approximately, the opening angle in elevation dimension of the surface of interest should be small, while no restriction is imposed to the azimuth dimension. Then, two kinds of deconvolution theories are built for SHB using the zero and the periodic boundary conditions respectively. Both simulations and experiments demonstrate that the periodic boundary condition is superior to the zero one, and fits the 3D acoustic source identification with solid spherical arrays better. Finally, four periodic boundary condition based deconvolution methods are formulated, and their performance is disclosed both with simulations and experimentally. All the four methods offer enhanced spatial resolution and reduced sidelobe contaminations over SHB. The recovered source strength approximates to the exact one multiplied with a coefficient that is the square of the focus distance divided by the distance from the source to the array center, while the recovered pressure contribution is scarcely affected by the focus distance, always approximating to the exact one.

Interferometric enhancement for forward-looking synthetic aperture radar target imaging

We present a technique for interferometric processing of extreme high-squint synthetic aperture radar (SAR) target images. The technique is an improvement over conventional SAR interferometry, as it takes into account the change in relative phase between channels that occur as the radar moves through the synthetic aperture, which can be significant for forward-looking data acquisition geometry. We apply the technique to simulate data from a variety of imaging cases and show that, unlike the conventional interferometric technique, the proposed technique is robust to synthetic aperture length, scatterer elevation, and scatterer dimension in forward-looking scenarios. The technique may be used for various applications related to situational awareness, such as remote sensing for collision avoidance or forward-looking reconnaissance.