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Dynamic deformation measurement is an important approach to monitor structure stability. Due to its rapid imaging capabilities, ground-based multi-input–multi-output (GB-MIMO) radar has shown great application potential in bridge structure monitoring. This article proposes a dynamic deformation estimation method based on radar image series. First, an improved clutter suppression method is used to overcome the estimation error in complex working status. Second, a two-step pixel extraction method is adopted to ensure both the quantity and quality of sample pixels. Finally, frequency- and time-domain environmental stimulating methods are jointly considered for stable mode shape estimation. This article systematically measured the dynamic deformations of bridge structures, including two suspension bridges and a cable-stayed bridge. The time-series deformation, deflection, vibration frequency, and amplitude on the bridge structure are obtained by image-domain measurement. For the first time, the mode shapes of bridges are obtained through GB-MIMO radar. The correctness of structure parameter measurement is verified with the finite element model (FEM). Experimental results prove that with the proposed method, stable results could be acquired against weak and complex stimulation conditions. [ABSTRACT FROM AUTHOR]

This article presents detailed geometric analyses of ground clutter in bistatic passive airborne radars. Analytic closed-form solutions are derived for finding the intersection of iso-ranges and iso-velocities. This allows clutter bistatic coordinates to be easily converted to its Cartesian coordinates. Based on those solutions, a theoretical clutter map in the bistatic coordinates can be calculated assuming uniform clutter distribution on the Earth’s surface. This is done by first converting a resolution cell in the bistatic coordinates into a corresponding cell in the Cartesian coordinates. Next, the power distribution in the bistatic coordinates is calculated taking into account range dependency, incident angles, and the radiation patterns of the transmitter and the receiver. The aim of this analysis is to characterize clutter in terms of expected mean power map rather than in terms of statistical distribution. The obtained results provide insights into the expected characteristics of clutter, which can be helpful in designing signal processing algorithms for ground moving target indication (GMTI) and synthetic aperture radar (SAR). The theoretical clutter distribution on the range-velocity map is compared with real-life data acquired with a Digital Video Broadcasting–Terrestrial (DVB-T)-based passive radar and good agreement between theory and measurement is presented. [ABSTRACT FROM AUTHOR]

The holographic subsurface radar (HSR) has been a promising geophysical electromagnetic technique for detecting shallowly buried targets with high lateral resolution image. However, radar images are considerably interpreted by strong reflections from rough surface and inhomogeneity in media of interest. In this article, we focus on mitigating the clutter in HSR applications using a learning-based approach, which requires neither prior information regarding the penetrable medium characteristics nor analytic framework to describe the through-medium interference. The generative adversarial network (GAN) with attentive subspace projection is developed to remove the clutter and recover the target image. The subspaces containing target response are selected with the multihead attention preliminarily. Then, the generative network will further focus on the target regions, and the discriminative network will assess the generated results locally and globally. Experiments using real data were conducted to demonstrate the effectiveness of our approach. The visual and quantitative results show that the proposed approach achieves superior performance on removing clutter in HSR images compared with the state-of-the-art clutter mitigation approaches. [ABSTRACT FROM AUTHOR]

To realize high azimuth resolution for sea-surface targets, this article proposes a superresolution imaging method that relies on the Weibull distribution. The proposed method introduces the generalized Gaussian distribution and Weibull distribution to represent the statistical distribution function of the target prior information and sea clutter, respectively. The corresponding objective function was derived under the maximum a posteriori (MAP) criterion. To address the nonlinearity of the objective function, this article adopts the Newton–Raphson iterative method to resolve it. Simulations and experimental data assessment indicate that the proposed method has superior superresolution imaging performance compared with other traditional superresolution methods for sea-surface target imaging. [ABSTRACT FROM AUTHOR]

Airborne multichannel high-resolution radar (HRR)-ground moving target indication (GMTI) is of great significance to wide-area surveillance, traffic monitoring, and target recognition. The Aerospace Information Research Institute, Chinese Academy of Sciences, produced an advanced airborne digital array radar with high resolution and conducted an experiment with slow and weak cooperative moving targets in 2021. In this article, an overall processing framework for target detection, parameter estimation, and target tracking using this system is introduced. For HRR detection, the echo energy of targets is spread into multiple range units, so-called range-spread targets; thus, using detectors designed for pointlike targets will severely degrade the detection performance, especially for slow and weak targets. To address the adaptive detection of such range-spread targets embedded in Gaussian clutter with an unknown covariance matrix, a novel two-step generalized space–time adaptive processing (GSTAP) algorithm is proposed, which offers an enhanced clutter suppression capability compared with natural competitors. Moreover, a tracking method is implemented in the range-Doppler domain to avoid track loss caused by azimuth relocation error and reject discrete false alarms. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method and provide a paradigm for further research. [ABSTRACT FROM AUTHOR]

Holographic subsurface radar (HSR) is a promising geophysical electromagnetic technique to detect shallowly buried objects due to its high lateral resolution. However, the subsurface inspections and visualization of buried targets are prone to be impaired by strong clutter from the nonplanar surface reflections. In this article, a clutter mitigation method using dual-frequency cancellation and sparse feature enhancement is proposed for HSR data to distinguish the targets from background. The radar signals are received at two distinct frequencies under certain conditions to calculate the strong surface reflections. After cancellation of the estimated surface, a modified $\ell _{1}$ regularization is utilized to further mitigate the residual clutter and highlight the target signature. The effectiveness of the proposed method is evaluated on both numerical simulation and radar signals collected from real HSR systems. The visual and quantitative results demonstrate that the proposed method successfully removes the nonplanar surface clutter with the targets preserved. [ABSTRACT FROM AUTHOR]

High-frequency surface wave radar (HFSWR) has become the cornerstone of maritime surveillance because of its low-cost maintenance and coverage of wide area. However, when it comes to the extraction of key areas, such as vessel-target detection and vessel-path tracking, the HFSWR signal is strongly interfered by clutters and noise, which makes maritime surveillance a challenging task. This article proposes a hierarchical key-area perception model for maritime surveillance harnessing range-Doppler (RD) image from HFSWR, Laplacian kernel, a linear classifier (LC), and a subnet-based multilayer representation learning framework (SMRLF). First, a weak LC with a Laplacian kernel is utilized to capture the plausible vessel regions (PVRs). Then, a novel SMRLF is proposed to localize the vessel targets from the PVRs. To handle the noise, a maximum correntropy criterion with variable centers (MCC-VC) is incorporated in the subnet-based learning model. A thorough experimental analysis on cross-domain samples from radar dataset to scene classification dataset shows that the proposed HKPM performs competitively. The model shows a superior performance over most of the state-of-the-art vessel-target detection algorithms with a vessel-target detection accuracy of 94%. The extended analysis on image classification problem proves that the proposed model has great adaptivity and scalability. [ABSTRACT FROM AUTHOR]

To suppress background clutter in short-range millimeter wave multiple-input multiple-output (MIMO) imaging, an adaptive and cost-efficient approach is proposed based on the availability of the background observation. By analyzing the origins of the background clutter, gain variation, and blocking effect are identified as the primary culprits responsible for the background clutter variation, which are treated separately in the proposed method. The gain variation is compensated with an adaptive power weighting (APW) procedure, where the gain variance is accurately and rapidly measured using the short-range profile segment associated with the interelement direct coupling. The clutter residual due to the blocking effect after APW is further removed by the combination of two zero-thresholding processes and coherence-factor-based image enhancement. The proposed method entails relatively light computational burden at the cost of acceptable memory increment, and is, thus, more amenable to real-time imaging requirement. Both simulations and experimental imaging tests on an MIMO array demonstrate the effectiveness and cost-efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Ground-based synthetic aperture radar (GBSAR) systems are widely used for monitoring slopes, especially in quarries and open pits. Unfortunately, the movement of the machinery (cranes, trucks, shovels, and excavators) in the radar field of view often prevents to correctly focus large circular sections of the radar images, producing a characteristic artifact, often named “blurring.” Therefore, the aim of this article is to propose both an acquisition modality and an image processing technique able to mitigate the blurring due to moving clutter. The proposed acquisition modality (named “free-running” or “on-the-fly”) allows to filter out the high-frequency clutter, which is often noticeable in GBSAR images, but it is not effective with low-frequency clutter caused by slow movement of machinery like heavy trucks or large cranes. For this reason, the authors propose even a suitable processing method that can be used in combination with high-frequency sampling. The application of these two methods has been proved to be very effective in realistic quarry scenario. [ABSTRACT FROM AUTHOR]

Through-the-wall radar imaging is a sensing technology that can be used by first responders to see through obscure barriers during search-and-rescue missions or deployed by law enforcement and military personnel to maintain situational awareness during tactical operations. However, the strong reflections from the front wall and other obstacles render the detection of stationary targets very difficult. In this article, a learning-based approach is proposed to mitigate the effect of the wall and background clutter. A sparse autoencoder with a low-rank projection is developed to mitigate the wall clutter and recover the target signal. The weights of the proposed autoencoder are determined by solving an augmented Lagrange multiplier optimization problem, and the regularization parameters are estimated using the Bayesian optimization technique. Experiments using real data from a stepped-frequency radar were conducted to illustrate its effectiveness for wall clutter removal. The results show that the proposed method achieves superior performance compared with the existing approaches. [ABSTRACT FROM AUTHOR]

Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction, and multipath clutter in indoor through-wall environments. Although several methods have been proposed for removing target-independent static and dynamic clutter, there still remain considerable challenges in mitigating target-dependent clutter especially when the knowledge of the exact propagation characteristics or analytical framework is unavailable. In this article, we focus on mitigating wall effects using a machine learning-based solution—denoising autoencoders—that does not require prior information of the wall parameters or room geometry. Instead, the method relies on the availability of a large volume of training radar images gathered in through-wall conditions and the corresponding clean images captured in line-of-sight conditions. During the training phase, the autoencoder learns how to denoise the corrupted through-wall images in order to resemble the free space images. We have validated the performance of the proposed solution for both static and dynamic human subjects. The frontal radar images of static targets are obtained by processing wideband planar array measurement data with 2-D array and range processing. The frontal radar images of dynamic targets are simulated using narrowband planar array data processed with 2-D array and Doppler processing. In both simulation and measurement processes, we incorporate considerable diversity in the target and propagation conditions. Our experimental results, from both simulation and measurement data, show that the denoised images are considerably more similar to the free-space images when compared to the original through-wall images. [ABSTRACT FROM AUTHOR]

Through-the-wall radar imaging (TWRI) is a sensing technology that can be used for detecting, locating, and identifying targets inside enclosed building structures. Many of the existing target classification approaches focus on single-target scene. For multi-target classification, the radar signal has to be segregated into different target components. However, target separation in TWRI is a challenging problem since the radar signals consist of both strong wall reflections and weak target echoes. Furthermore, the target signals are attenuated and distorted when propagating through the wall. In this paper, a variational model with low-rank constraint is proposed for decomposing the radar signal into target components and removing the wall returns. Experimental results show that the proposed method can effectively separate the radar signal into different target components. [ABSTRACT FROM AUTHOR]

Penetrating radar systems are widely employed to scan the objects that are placed behind or buried inside mediums (such as walls, ground, and so on). As the clutter is much stronger than the target echo, clutter removal must be performed before imaging. The moving average subtraction, spatial notch filtering, and singular value decomposition methods are commonly used to remove clutter. However, the drawback is that these methods eliminate some of the target spectrum information, which causes target energy losses and generates side lobes. To solve this problem, two spectrum recovery methods are proposed in this paper. The first method recovers the spectrum magnitude and phase via sinc interpolation and linear fitting, respectively, which is fast and suitable for real-time processing. Although the second method recovers the spectrum based on matrix completion with prior information, which is more accurate and more computational expensive. Extensive simulations and experiments are presented to validate the proposed methods. The results show that the proposed methods can improve various traditional clutter removal methods, the side lobes are clearly suppressed, and the signal-to-clutter ratio is significantly improved. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is an advanced imaging sensor that can image side-looking terrain. Until now, SAR schemes and imaging theories have been researched for stationary scenes. Unlike a stationary scene, due to unknown motion parameters, a ground moving target (GMT) is shifted and defocused in a background image, which causes difficulties in their use for further applications. This paper proposes a multiple pulse repetition frequency (PRF) airborne SAR scheme for background-free GMT imaging. In the proposed scheme, pulses are transmitted by a nonuniform pulse repetition time, and echoes are divided into groups with different PRFs. For spectra with different PRFs, due to their diverse respective spectra extending periods, periodical extending GMT spectra will not appear at the same location except for the original spectra. A theorem is proposed to prove that the spectra can be extracted intact, undistorted, and unpolluted. In light of the divergence of the GMT spectra location from that of the clutter, filter banks are designed to locate, extract, and reconstruct the GMT spectra out of the mixed spectra that contain the GMT and clutter. For GMT SAR imaging, a moving target is regarded as a steady target under an equivalent geometry with a new equivalent squint angle and a new SAR platform velocity. The SAR image is obtained with the range-Doppler algorithm with equivalent geometry parameters that are estimated from the Doppler parameters. Experimental results obtained from using extensive numerical simulated data validate our proposed approach. [ABSTRACT FROM AUTHOR]

Detection and localization of stationary targets behind walls is primarily challenged by the presence of the overwhelming electromagnetic signature of the front wall in the radar returns. In this paper, we use the discrete prolate spheroidal sequences to represent spatially extended stationary targets, including exterior walls. This permits the formation of a linear block sparse model relating the range profile and observation vectors. Effective wall clutter suppression can then be performed prior to sparse signal image reconstruction. We consider stepped-frequency radar with two cases of frequency measurement distributions over antenna positions. In the first case, the same subset of frequencies is used for each antenna in physical or synthetic aperture arrays, whereas the other case allows different sets of few frequency observations to be available at different antennas. Using experimental data, we demonstrate that the proposed scheme enables sparsity-based image reconstruction techniques to effectively detect and localize behind-the-wall stationary targets from reduced measurements. [ABSTRACT FROM PUBLISHER]

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]

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]