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Ground penetrating radar (GPR) image inversion is of great significance for interpreting GPR data. In practical applications, the complexity and nonuniformity of underground structures bring noise and clutter interference, making GPR inversion problems more challenging. To address these issues, this study proposes a two-stage GPR image inversion network called MHInvNet based on multi-scale dilated convolution (MSDC) and hybrid attention gate (HAG). This method first denoises the B-scan through the first network MHInvNet1, then combines the denoised B-scan from MHInvNet1 with the undenoised B-scan as input to the second network MHInvNet2 for inversion to reconstruct the distribution of the permittivity of underground targets. To further enhance network performance, the MSDC and HAG modules are simultaneously introduced to both networks. Experimental results from simulated and actual measurement data show that MHInvNet can accurately invert the position, shape, size, and permittivity of underground targets. A comparison with existing methods demonstrates the superior inversion performance of MHInvNet. [ABSTRACT FROM AUTHOR]

The fusion of synthetic aperture radar (SAR) and optical satellite imagery poses significant challenges for ship detection due to the distinct characteristics and noise profiles of each modality. Optical imagery provides high-resolution information but struggles in adverse weather and low-light conditions, reducing its reliability for maritime applications. In contrast, SAR imagery excels in these scenarios but is prone to noise and clutter, complicating vessel detection. Existing research on SAR and optical image fusion often fails to effectively leverage their complementary strengths, resulting in suboptimal detection outcomes. This research presents a novel fusion framework designed to enhance ship detection by integrating SAR and optical imagery. This framework incorporates a detection system for optical images that utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) in combination with the YOLOv7 model to improve accuracy and processing speed. For SAR images, a customized Detection Transformer model, SAR-EDT, integrates advanced denoising algorithms and optimized pooling configurations. A fusion module evaluates the overlaps of detected bounding boxes based on intersection over union (IoU) metrics. Fused detections are generated by averaging confidence scores and recalculating bounding box dimensions, followed by robust postprocessing to eliminate duplicates. The proposed framework significantly improves ship detection accuracy across various scenarios. [ABSTRACT FROM AUTHOR]

Millimeter-wave automotive radar has become an essential tool for autonomous driving, providing reliable sensing capabilities under various environmental conditions. To reduce hardware size and cost, sparse arrays are widely employed in automotive radar systems. Additionally, because the targets detected by automotive radar typically exhibit sparsity, compressed sensing-based algorithms have been utilized for sparse array reconstruction, achieving superior performance. However, traditional compressed sensing algorithms generally assume that targets are located on a finite set of grid points and perform sparse reconstruction based on predefined grids. When targets are off-grid, significant off-grid errors can occur. To address this issue, we propose an automotive radar sparse reconstruction algorithm based on accelerated Atomic Norm Minimization (ANM). By using the Iterative Vandermonde Decomposition and Shrinkage Threshold (IVDST) algorithm, we can achieve fast ANM, which effectively mitigates off-grid errors while reducing reconstruction complexity. Furthermore, we adopt a Generalized Likelihood Ratio Test (GLRT) detector to eliminate noise and clutter in the automotive radar operating environment. Simulation results show that our proposed algorithm significantly improves reconstruction accuracy compared to the iterative soft threshold (IST) algorithm while maintaining the same computational complexity. The effectiveness of the proposed algorithm in practical applications is further validated through real-world data experiments, demonstrating its superior capability in clutter elimination. [ABSTRACT FROM AUTHOR]

Background: Digital breast tomosynthesis (DBT) has outpaced digital mammography in clinical adoption in the United States; however, substantial technological limitations remain to image quality in DBT, including undersampling from a one‐dimensional (1D) scan geometry, x‐ray source motion during acquisition, and patient motion artifacts from long exam times. Purpose: A thermionic cathode x‐ray system employing two‐dimensional (2D, planar) multiple x‐ray‐source arrays (MXA) is proposed to improve DBT image quality. Methods: A 1D MXA, consisting of a linear array of thermionic cathodes was used to simulate a 2D MXA. The 1D MXA included 11 focal spots separated by a distance of Δd${{\Delta}}d$ = 23 mm. The 11 cathodes were paired with 11 molybdenum 50 mm diameter anode disks, mounted on a rotating shaft within a single vacuum enclosure. Image quality was investigated as a function of MXA configuration by integrating the 1D MXA with a 200 × 250 mm2 flat panel detector at a source‐to‐detector distance of 630 mm, resulting in a 20° tomographic arc. To simulate a 2D MXA, the detector (with phantom) was translated orthogonally to the linear array by a distance (δ$\delta $) ranging from δ$\delta $ = 0 mm (conventional 1D) to δ$\delta $ = 57 mm. All sources operated at 30 kV with 80 mA and 4.5 mAs/pulse, yielding ∼100 mAs per DBT dataset. DBT reconstructions involved 22 projections and used filtered backprojection with a ramp and Hann apodization filter. Volumetric reconstructions for each source were weighted by sampling differences between sources, and averaged. Image quality was assessed in terms of contrast‐to‐noise ratio (CNR), background clutter noise and power spectrum, and slice sensitivity profile (SSP) using a set of physical phantoms, including: (i) contrast‐detail signals coupled to spherical clutter (PMMA in air); (ii) an SSP phantom; (iii) a commercial "breast" phantom (CIRS BR3D, Sun Nuclear, Norfolk, VA); and (iv) bovine muscle. Results: Background clutter noise amplitude reduced monotonically from the 1D MXA (σclutter = 5.9 A.U., δ$\delta $ = 0 mm) and 2D MXA arrays with increasing δ$\delta $, with statistical significance between the 1D MXA and 2D MXA with δ$\delta $ = 57 mm (σclutter = 5.0 A.U., p < 0.001). The contrast‐detail/clutter phantom demonstrated CNR from the 2D MXA (δ = 57 mm) outperforming the 1D MXA in all combinations of contrast and detail. 2D power spectrum analysis of clutter demonstrated a pronounced Fourier domain null cone for the 1D MXA in the anterior field‐of‐view (away from the 1D MXA position), whereas the 2D MXA geometry (δ = 57 mm) did not exhibit the null cone. The SSP was 15%–50% narrower (FWHM) for the 2D versus the 1D geometry, across all reconstruction setups. Conclusions: The advantages of a 2D source geometry for DBT imaging were demonstrated quantitatively compared to a conventional 1D line of x‐ray sources. The improvement in the 2D geometry was attributed both to improved Fourier domain sampling and reduced SSP. We conclude that 2D MXA sources have the potential to substantially improve DBT imaging in comparison to existing commercial DBT systems. [ABSTRACT FROM AUTHOR]

Star trackers are susceptible to interference from stray light, such as sunlight, moonlight, and Earth atmosphere light, in the space environment, resulting in an overall improvement in the star image grayscale, poor background uniformity, low star extraction rate, and high number of false star spots. In response to these challenges, this paper proposes a grayscale iterative star spot extraction algorithm based on image entropy. The implementation of the algorithm is mainly divided into two steps: (1) The algorithm conducts multiple grayscale iterations, effectively utilizing the prior information on the local contrast of star spots to filter out stray light backgrounds to a certain extent. (2) By establishing an inner–outer template, the image entropy algorithm is employed to obtain the real star targets to be extracted, which further suppresses the background clutter and noise. Numerical simulations and experimental results demonstrate that, compared to traditional detection algorithms, this algorithm can effectively suppress background stray light, enhance star extraction rates, and reduce the number of false star spots, and it exhibits superior detection performance in complex backgrounds across various scenarios. [ABSTRACT FROM AUTHOR]

Ship detection, a crucial task, relies on the traditional CFAR (Constant False Alarm Rate) algorithm. However, this algorithm is not without its limitations. Noise and clutter in radar images introduce significant variability, hampering the detection of objects on the sea surface. The algorithm's theoretically Constant False Alarm Rates are not upheld in practice, particularly when conditions change abruptly, such as with Beaufort wind strength. Moreover, the high computational cost of signal processing adversely affects the detection process's efficiency. In previous work, a four-stage methodology was designed: The first preprocessing stage consisted of image enhancement by applying convolutions. Labeling and training were performed in the second stage using the Faster R-CNN architecture. In the third stage, model tuning was accomplished by adjusting the weight initialization and optimizer hyperparameters. Finally, object filtering was performed to retrieve only persistent objects. This work focuses on designing a specific methodology for ship detection in the Peruvian coast using commercial radar images. We introduce two key improvements: automatic cropping and a labeling interface. Using artificial intelligence techniques in automatic cropping leads to more precise edge extraction, improving the accuracy of object cropping. On the other hand, the developed labeling interface facilitates a comparative analysis of persistence in three consecutive rounds, significantly reducing the labeling times. These enhancements increase the labeling efficiency and enhance the learning of the detection model. A dataset consisting of 60 radar images is used for the experiments. Two classes of objects are considered, and cross-validation is applied in the training and validation models. The results yield a value of 0.0372 for the cost function, a recovery rate of 94.5%, and an accuracy rate of 95.1%, respectively. This work demonstrates that the proposed methodology can generate a high-performance model for contact detection in commercial radar images. [ABSTRACT FROM AUTHOR]

This manuscript presents a novel framework for high-resolution and robust microwave correlation imaging. In order to generate a more diverse random radiation field distribution, the unified random radiation field (URRF) model is proposed. The URRF model can accurately characterize the joint random modulation in the signals' phase, amplitude, and frequency. Furthermore, we build a parametric imaging model based on URRF which clearly describes the relationship between the image to be reconstructed and the signals by the URRF model. By using this imaging model, the reconstruction of an image is converted into solving a multi-parameter optimization problem with multiple constraints. To solve this optimization problem with high efficiency and accuracy, the model-constrained adaptive alternating multiple parameter estimation (MC-AAMPE) algorithm is proposed. This algorithm decomposes the high-dimensional multi-parameter optimization problem into several sub-optimization problems. The renewing solutions to these sub-optimization problems make the multi-parameter optimization converge to the image of the target and the parameters of clutter and noise, which are all unknown before the solution. In comparison with the existing methods, the proposed scheme generates images with higher resolution and is more robust under noise conditions. Extensive simulation experiments confirmed the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

The spectral detection of weak frequency band signals poses a serious problem in many applications, especially when the target is within a certain frequency band under low signal-to-noise ratio (SNR) conditions. A kind of novel technique based on the pre-whitening scale transformation of stochastic resonance (SR) in a symmetric bistable system in a parallel configuration is proposed to solve the problem. Firstly, pre-whitening can ensure the Gaussian distribution of the receiving signal fits the requirements for SR processing. Secondly, scale transformation can help to effectively utilize the properties of a weak signal, especially under a low-frequency band. Thirdly, the SR in a symmetric bistable system in a parallel configuration can try to smoothly reduce the variances in the clutter and additive noise. Fourthly, by subtracting the steady state response of the SR in the selected symmetric bistable system from the parallel output, the spectral detection of a weak signal can be realized successfully. Experiment results based on actual sea clutter radar data guarantee the effectiveness and applicability of the proposed symmetric bistable PSR processing approach. [ABSTRACT FROM AUTHOR]

The high resolution range profile (HRRP) of radar air target often contains a certain amount of clutter noise, and it is necessary to focus on the influence of noise to carry out air target recognition using HRRP. To address the above issues, an air target HRRP recognition method based on deep residual shrinkage network (DRSN) is proposed, which combines deep residual network, soft threshold mg function and attention mechanism, and cross-layer identify connection method is adopted. DRSN can not only avoid the problem of gradient vanishing or gradient exploding caused by too deep layers of the network, which leads to the degradation of the teaming ability of the network, but also can effectively After out the influence of noisy features in the recognition process, so that the model can focus on the recognition of deep features in the target region and improve the recognition ability of the model in the strong noise background. The experimental results show that the proposed method has certain advantages n recognition effort under each signal -to-noise ratio condition compared with other commonly used deep tearing models, and the model has strong robustness to noise. [ABSTRACT FROM AUTHOR]

To solve the problem on low classification accuracy caused by the difficulty of extracting features of micro-motion targets, such as pedestrians, vehicles, and drones in low-altitude radar monitoring scenarios, a multi-channel radar target micro-motion feature classification method based on EfficientNet is proposed. Firstly, a multi-energy singular value decomposition method is proposed to suppress clutter and noise, and enhance the micro-motion characteristics of the target based on the energy distribution differences between the clutter, target, and noise signals. Secondly, the multi-channel EfficientNet model is designed to combine time-frequency information features in radar sum and difference channels, and further fuse multi-channel micro-motion features to achieve accurate target classification. Finally, the effectiveness of the proposed method is visited through experiments using radar measured target data. The results show that compared with other methods, the proposed method significantly improves classification accuracy with low model complexly. [ABSTRACT FROM AUTHOR]

To address the heterogeneous clutter environment of airborne early warning radar in space-time adaptive processing (STAP), a direct data domain (D3) STAP method via joint-sparse characteristics of clutter and noise is proposed. Firstly, training sample set is expanded using the sub-aperture smoothing technique. Then, the STAP power dictionary matrix is constructed based on the second-order characterization theory of the clutter spectrum, and the objective function in derived. Meanwhile, the space-time power spectrum of the signal in the cell under test is solved. Finally, the clutter plus noise covariance matrix without aperture loss is reconstructed according to the clutter prior information. Numerical experiments show that the estimation accuracy of the proposed method is higher than that of the traditional sparse recovery D3-STAP algorithm, and the proposed method has better performance of the low-speed target detection under ideal conditions and array errors. [ABSTRACT FROM AUTHOR]

The accurate measurement of the wall shear rate (WSR) plays a crucial role in the early diagnosis of cardiovascular disease progression and acute events such as aneurysms and atherosclerotic plaque ruptures. To address this need, the speckle decorrelation (SDC) technique has been used to measure WSR based on the 2-D out-of-plane blood flow speed. This technique is particularly advantageous because it enables the use of a 1-D array transducer to measure WSR over the entire luminal area. This study aims to develop a region-based singular value decomposition (SVD) filtering technique that selectively suppresses clutter noise in the vascular region to measure WSR using SDC. Ultrasound simulations, in-vitro flow experiments, and an in-vivo human study were conducted to evaluate the feasibility of this method's clinical application. The results demonstrated that WSR can be effectively measured across entire vascular walls using a conventional 1-D array transducer along with the proposed methodology. This study successfully demonstrates a noninvasive and accurate SDC-based method for measuring vital vascular WSR. This approach holds significant promise for assessing vascular WSR in both healthy individuals and high-risk cardiovascular disease patients. [ABSTRACT FROM AUTHOR]

In this paper, an ultrasound beamforming method for plane wave (PW) imaging based on modified sidelobe blanking (MSLB) is proposed to improve image resolution and contrast ratio (CR). In this framework, PWs from various angles were designed to create main and auxiliary beamformer signals. Specifically, the PW signals from all angles were first coherently combined to serve as the main beamformer output signals. To prevent excessive clutter and noise, output signals in the main beamformer were weighted by the generalized coherence factor. Subsequently, the PW signals were split into positive and negative angles to perform a subtraction, creating the auxiliary beamformer. Finally, signals in the main beamformer were compared with the signals in the auxiliary beamformer point by point to further eliminate the noises and clutters. Compared with the delay and sum, full width at half maximum of the MSLB for point targets was reduced by an average of 54.17% and 51.65% in simulations and experiments, respectively; and the corresponding CR was improved by 55.38% and 18.40% on average. The MSLB method provided better imaging quality in human carotid arteries. In conclusion, the proposed method can effectively improve image resolution and CR with low computational complexity. [ABSTRACT FROM AUTHOR]

Accurate powerline classification from LiDAR point clouds is essential for efficient monitoring and management of power distribution networks. Currently, the classification is being done through manual labeling or some semi-automatic methods which are time-consuming and resource intensive. In this study, we explore three deep learning architectures, namely KPConv, PointCNN, and RandLA-Net, for powerline segmentation in aerial and mobile LiDAR datasets. We utilize manually labeled aerial and mobile LiDAR datasets from Surry in Canada and Kerala in India, respectively. Impressive results are obtained, demonstrating high powerline classification accuracy and precision. KPConv outperformed the other architectures, achieving over 98% accuracy for ALS data and surpassing 94% for MLS data. Additionally, we investigate the impact of ground removal on powerline extraction. The results highlight a significant improvement in powerline classification accuracy, with an approximate 3–4% enhancement observed, particularly in the test sets with a higher density of powerline points. Removing ground points effectively reduces noise and clutter in LiDAR data, leading to improved segmentation outcomes. Furthermore, ground removal proves beneficial in classifying wires in challenging scenarios, such as areas with nearby objects or branch and loose wires. KPConv demonstrates superior performance in powerline classification, capturing intricate powerline details even in scenes with varying point densities. PointCNN shows limitations, especially in wire point classification near other objects. RandLA-Net exhibits higher speed but slightly lower accuracy compared to KPConv. The findings contribute to advancing powerline classification and provide valuable insights for more efficient and automated management of power distribution networks. [ABSTRACT FROM AUTHOR]

In view of the low signal-to-noise ratio (SNR) of shear wave electromagnetic acoustic transducers (EMAT) in the detection of high-temperature equipment, the use of low excitation voltage (LEV) further deteriorates the detection results, resulting in the echo signal containing defects being drowned in noise. For the extraction of the EMAT signal, an adaptive noise reduction method is proposed. Firstly, the minimum envelope entropy is taken as the fitness function for the Harris Hawks Optimizer (HHO), and the optimal successive variational mode decomposition (SVMD) balance parameter is searched by HHO adaptive iteration to decompose LEV EMAT signals at high temperatures. Then the filter is carried out according to the excitation center frequency and correlation coefficient threshold function. Then, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the filtered signal and combine the kurtosis factor to select the appropriate intrinsic mode functions. Finally, the signal is extracted by the Hilbert transform. In order to verify the effectiveness of the method, it is applied to the low-voltage detection of 40Cr from 25 °C to 700 °C. The results show that the method not only suppresses the background noise and clutter noise but also significantly improves the SNR of EMAT signals, and most importantly, it is able to detect and extract the 2 mm small defects from the echo signals. It has great application prospects and value in the LEV detection of high-temperature equipment. [ABSTRACT FROM AUTHOR]

The authors deal with the problem of the design and manufacture of a high‐resolution FMCW radar for drone detection and classification. The difficulties of the problem are discrete clutter reduction and spurious phase noise mitigation. The discrete clutter is due to the reflected signals from land vehicles, birds etc., while the spurious phase noise is inherent in the radar signal due to the phase‐locked loop component and leakage between the transmitting and receiving paths. Both spurious phase noise and clutter will increase the system noise level and hence reduce the probability of detection of small targets such as drones and induce false alarms on the radar screen. In order to reduce discrete clutter, the authors propose a method to separate a drone from discrete clutter based on the design of the radar system parameters for a drone and its propeller detection, target's Doppler dispersion and moving characteristics. For spur mitigation, a method that focuses on the design of the isolation coefficient between transmitting and receiving paths to decrease the power of spurs below the minimum power requirement at the input of the analogue‐to‐digital converter is introduced. The results were applied by the authors to the development and manufacture of a radar with the given specifications for drone detection and classification. Different laboratory and field tests show that the spurs are mitigated and the drones are separated from discrete clutter with a range and accuracy better than the one recently published. [ABSTRACT FROM AUTHOR]

Space–time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. When working in the heterogeneous environment, the number of training samples that satisfy independent and identically distributed (IID) conditions is insufficient, making it difficult to ensure the estimation accuracy of the clutter plus noise covariance matrix for traditional STAP methods. Sparse recovery–based STAP (SR–STAP) methods have received widespread attention in the past few years. The accurate estimation of the clutter plus noise covariance matrix can be achieved using only a few training samples. The iterative adaptive approach (IAA) can quickly and accurately estimate the power spectrum, but applying this method directly to the STAP method cannot produce good performance. In this paper, a fast IAA–based SR–STAP method is proposed. Based on the weighted l 1 problem, the IAA spectrum is used as a weighted term to obtain a good approximation. In order to obtain an analytical solution, we use the weighted l 2 norm to approximate the weighted l 1 norm without loss of performance. Compared with the IAA–STAP method, the proposed method is more robust to errors. Moreover, the proposed method has a fast computational speed. The effectiveness of the proposed method is demonstrated by simulations. [ABSTRACT FROM AUTHOR]

Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial–temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial–temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model. [ABSTRACT FROM AUTHOR]

Ship detection using synthetic aperture radar (SAR) images is widely applied to marine monitoring, ship identification, and other intelligent maritime applications. It also improves shipping efficiency, reduces marine traffic accidents, and promotes marine resource development. Land reflection and sea clutter introduce noise into SAR imaging, making the ship features in the image less prominent, which makes the detection of multi-scale ship targets more difficult. Therefore, a cross-scale ship detection network for SAR images based on efficient receptive field and enhanced hierarchical fusion is proposed. In order to retain more information and lighten the weight of the network, an efficient receptive field feature extraction backbone network (ERFBNet) is designed, and the multi-channel coordinate attention mechanism (MCCA) is embedded to highlight the ship features. Then, an enhanced hierarchical feature fusion network (EHFNet) is proposed to better characterize the features by fusing information from lower and higher layers. Finally, the feature map is input into the detection head with improved bounding box loss function. Using SSDD and HRSID as experimental datasets, average accuracies of 97.3% and 90.6% were obtained, respectively, and the network performed well in most scenarios. [ABSTRACT FROM AUTHOR]

Echolocating big brown bats (Eptesicus fuscus) broadcast downward frequency-modulated sweeps covering the ultrasonic range from 100–23 kHz in two harmonics. They perceive target range from the time delay between each broadcast and its returning echo. Previous experiments indicated that the bat's discrimination acuity for broadcast-echo delay declines when the lowest frequencies (23–35 kHz) in the first harmonic of an echo are removed. This experiment examined whether echo detection is similarly impaired. Results show that detection thresholds for echoes missing these lowest frequencies are raised. Increased thresholds for echoes differing in spectra facilitates the bat's ability to discriminate against clutter. [ABSTRACT FROM AUTHOR]

The space–time adaptive processing (STAP) technique can effectively suppress the ground clutter faced by the airborne radar during its downward-looking operation and thus can significantly improve the detection performance of moving targets. However, the optimal STAP requires a large number of independent identically distributed (i.i.d) samples to accurately estimate the clutter plus noise covariance matrix (CNCM), which limits its application in practice. In this paper, we fully consider the heterogeneity of clutter in real-world environments and propose a sparse Bayesian learning-based reduced-dimension STAP method that achieves suboptimal clutter suppression performance using only a single sample. First, the sparse Bayesian learning (SBL) algorithm is used to estimate the CNCM using a single training sample. Second, a novel angular Doppler channel selection algorithm is proposed with the criterion of maximizing the output signal-to-clutter-noise ratio (SCNR). Finally, the reduced-dimension STAP filter is constructed using the selected channels. Simulation results show that the proposed algorithm can achieve suboptimal clutter suppression performance in extremely heterogeneous clutter environments where only one training sample can be used. [ABSTRACT FROM AUTHOR]

The advent of deep learning has significantly propelled the utilization of neural networks for Synthetic Aperture Radar (SAR) ship detection in recent years. However, there are two main obstacles in SAR detection. Challenge 1: The multiscale nature of SAR ships. Challenge 2: The influence of intricate near-shore environments and the interference of clutter noise in offshore areas, especially affecting small-ship detection. Existing neural network-based approaches attempt to tackle these challenges, yet they often fall short in effectively addressing small-ship detection across multiple scales and complex backgrounds simultaneously. To overcome these challenges, we propose a novel network called SwinT-FRM-ShipNet. Our method introduces an integrated feature extractor, Swin-T-YOLOv5l, which combines Swin Transformer and YOLOv5l. The extractor is designed to highlight the differences between the complex background and the target by encoding both local and global information. Additionally, a feature pyramid IEFR-FPN, consisting of the Information Enhancement Module (IEM) and the Feature Refinement Module (FRM), is proposed to enrich the flow of spatial contextual information, fuse multiresolution features, and refine representations of small and multiscale ships. Furthermore, we introduce recursive gated convolutional prediction heads (GCPH) to explore the potential of high-order spatial interactions and add a larger-sized prediction head to focus on small ships. Experimental results demonstrate the superior performance of our method compared to mainstream approaches on the SSDD and SAR-Ship-Dataset. Our method achieves an F1 score, mAP0.5, and mAP0.5:0.95 of 96.5% (+0.9), 98.2% (+1.0%), and 75.4% (+3.3%), respectively, surpassing the most competitive algorithms. [ABSTRACT FROM AUTHOR]

Determining the source of a weak sound signal can be difficult, particularly in busy or noisy surroundings. The hand-engineered characteristics and algorithms used in traditional methods for source separation and localization are irresistible to changes in the signal or the noise. In environmental sound classification(ESC) the effectiveness of representative features collected from the environmental sounds is crucial to the classification performance. However, semantically meaningless frames and silent frames frequently hinder the performance of ESC. In order to address this problem, we proposed a new context-aware attention-based neural network for weak environmental sound source identification. Our method is based on the idea that the attention mechanism will be able to concentrate on important elements of the input signal and suppress irrelevant ones, enabling the network to locate the weak sound's source more effectively. To solve the limited capacity problem faced by the attention maps of the attention model we are using context information as additional input. The identification of weak signals and finding corresponding context information is also a challenging problem because of the degradation or noise in the signal. To solve this issue we proposed a novel algorithm based on MFCC for context feature generation. Additionally, the robustness and generalizability of the classification model are improved by using multiple feature extraction techniques which reduces the reliance on any single feature extraction technique. We test our methodology with experiments on datasets of simulated weak sound signals with varying amounts of noise and clutter. We evaluated the performance of our attention-based neural network in comparison to a number of established techniques. Our findings demonstrate that, especially in noisy and congested environments, the proposed model outperforms the baselines in terms of source identification accuracy. Overall, our work illustrates the efficiency of employing an attention-based neural network based on context information for the identification of weak sound sources and implies that this strategy may be a promising approach for further research in this area. [ABSTRACT FROM AUTHOR]

The task of the detection of unmanned aerial vehicles (UAVs) is of great significance to social communication security. Infrared detection technology has the advantage of not being interfered with by environmental and other factors and can detect UAVs in complex environments. Since infrared detection equipment is expensive and data collection is difficult, there are few existing UAV-based infrared images, making it difficult to train deep neural networks; in addition, there are background clutter and noise in infrared images, such as heavy clouds, buildings, etc. The signal-to-clutter ratio is low, and the signal-to-noise ratio is low. Therefore, it is difficult to achieve the UAV detection task using traditional methods. The above challenges make infrared UAV detection a difficult task. In order to solve the above problems, this work drew upon the visual processing mechanism of the human brain to propose an effective framework for UAV detection in infrared images. The framework first determines the relevant parameters of the continuous-coupled neural network (CCNN) through the image's standard deviation, mean, etc. Then, it inputs the image into the CCNN, groups the pixels through iteration, then obtains the segmentation result through expansion and erosion, and finally, obtains the final result through the minimum circumscribed rectangle. The experimental results showed that, compared with the existing most-advanced brain-inspired image-understanding methods, this framework has the best intersection over union (IoU) (the intersection over union is the overlapping area between the predicted segmentation and the label divided by the joint area between the predicted segmentation and the label) in UAV infrared images, with an average of 74.79% (up to 97.01%), and can effectively realize the task of UAV detection. [ABSTRACT FROM AUTHOR]

The Multiple Input Multiple Output (MIMO) radar, as a new type of radar, emits orthogonal waveforms, which provide it with waveform diversity characteristics, leading to increased degrees of freedom and improved target detection performance. However, it also poses challenges such as difficulty in meeting higher data demand, separating waveforms, and suppressing the multidimensional sidelobes (range sidelobes, Doppler sidelobes, and angle sidelobes) of targets. Phase-coded signals are frequently employed as orthogonal transmission signals in the MIMO radar. However, these signals exhibit poor Doppler sensitivity, and the intra-pulse Doppler frequency shift can have an impact on the effectiveness of the matching filtering process. To address the aforementioned concerns, this paper presents a novel approach called the Space–Time–Range Joint Adaptive Focusing and Detection (STRJAFD) method. The proposed method utilizes the Mean Square Error (MSE) criterion and integrates spatial, temporal, and waveform dimensions to achieve efficient adaptive focusing and detection of targets. The experimental results demonstrate that the proposed method outperforms conventional cascaded adaptive methods in effectively addressing the matching mismatch issue caused by Doppler frequency shift, achieving super-resolution focusing, possessing better suppression effects on three-dimensional sidelobes and clutter, and exhibiting better detection performance in low signal-to-clutter ratio and low signal-to-noise ratio environments. Furthermore, STRJAFD is unaffected by coherent sources and demands less data. [ABSTRACT FROM AUTHOR]

Recently, methods based on deep learning have been applied to target detection using synthetic aperture radar (SAR) images. However, due to the SAR imaging mechanism and low signal-clutter-noise-ratio (SCNR), it is still a challenging task to perform aircraft detection using SAR imagery. To address this issue, a novel aircraft detection method is proposed for low SCNR SAR images that is based on coherent scattering enhancement and a fusion attention mechanism. Considering the scattering characteristics discrepancy between human-made targets and natural background, a coherent scattering enhancement technique is introduced to heighten the aircraft scatter information and suppress the clutter and speckle noise. This is beneficial for the later ability of the deep neural network to extract accurate and discriminative semantic information about the aircraft. Further, an improved Faster R-CNN is developed with a novel pyramid network constructed by fusing local and contextual attention. The local attention adaptively highlights the significant objects by enhancing their distinguishable features, and the contextual attention facilitates the network to extract distinct contextual information of the image. Fusing the local and contextual attention can guarantee that the aircraft is detected as completely as possible. Extensive experiments are performed on TerraSAR-X SAR datasets for benchmark comparison. The experimental results demonstrate that the proposed aircraft detection approach could achieve up to 91.7% of average precision in low SCNR, showing effectiveness and superiority over a number of benchmarks. [ABSTRACT FROM AUTHOR]

Non-destructive testing of tree roots has the vast application potential to the root system evaluation of fruit and ancient trees for the better management of plant health. Among them, ground-penetrating radar (GPR) can be expected to nondestructively detect the tree roots, due to the portable and low-cost. However, the interpretation of GPR data is dependent mainly on the manual analysis, leading to the reduced automation and accuracy. Moreover, the clutters and noise that caused by soil heterogeneity can severely affect the accuracy of root detection and identification. In this study, a detection method was proposed for the clutter suppression and root parameter prediction of ground penetrating radar. The data collection was divided into two parts: the dataset and field test. The datasets consisted of simulated and synthesized real data. The roots with the radius ranging from 5 to 30 mm were randomly distributed within 300 mm underground. The field experiment was conducted in three sand pits. The soil heterogeneity was simulated to create the different moisture levels, when adding water into different parts in two of the dry sand pits. The rest sandpit was a condition of higher water content after heavy rainfall. At the same time, the roots were excavated to verify the model under real soil conditions. Four test pits contained the roots of different tree species, the different soil environments, as well as the different radii and depths. The attention mechanism was integrated into the U-net model, in order to enhance the capability in the clutter suppression and root target reflection restoration. A clutter suppression network was constructed to test the effectiveness of the model. The encoding-decoding structure of U-net was separated the target root hyperbolic reflection from the overall background. The attention mechanism enabled the network to better adaptively focus on the hyperbolic reflection of the target root system without identifying the clutter as the target reflection. The clutter suppression was realized for the original B-scan image to remove the adverse effects, due to the soil heterogeneity and radar antenna coupling. Furthermore, the network of root parameter prediction was constructed using residual blocks and inception. The multi-scale receptive field of inception was used to extract the global and local features, and simultaneously to predict the root radius and depth. The predicted results were obtained by parallel inputting the clutter suppressed image and the original B-scan image into the network. The effectiveness of the model was evaluated using datasets and field experiments. The test results of clutter suppression were evaluated using peak signal to noise ratio (PSNR) and structural similarity (SSIM), which were 39.42 dB and 0.991, respectively, better than before. The test results on datasets showed the mean absolute error (MAE) of 1.7 mm, and the coefficient of determination R² value of 0.914 for the root radius prediction, while the mean absolute error (MAE) of 6.3 mm and the R² value of 0.989 for the root depth prediction. The clutter suppression was also achieved in the better performance than the rest on the field data. The maximum prediction errors for the root radius and depth were 1.85, and 13.6 mm, respectively, where the total average relative error was 6.55%. Influencing factors were determined for the prediction effectiveness of ground-penetrating radar. The future research directions were pointed out as well. The findings can be applied to the actual prediction of root depth and radius of tree roots, particularly for the health administration of fruit trees and the protection of ancient trees.protection of ancient trees.protection of ancient trees. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Passive Bistatic Radar (PBR) has significant civilian and military applications due to its ability to detect low-altitude targets. However, the uncontrollable characteristics of the transmitter often lead to subpar target detection performance, primarily due to a low signal-to-noise ratio (SNR). Coherent accumulation typically has limited ability to improve SNR in the presence of strong noise and clutter. In this paper, we propose an adversarial learning-based radar signal enhancement method, called radar signal enhancement generative adversarial network (RSEGAN), to overcome this challenge. On one hand, an encoder-decoder structure is designed to map noisy signals to clean ones without intervention in the adversarial training stage. On the other hand, a hybrid loss constrained by L 1 regularization, L 2 regularization, and gradient penalty is proposed to ensure effective training of RSEGAN. Experimental results demonstrate that RSEGAN can reliably remove noise from target information, providing an SNR gain higher than 5 dB for the basic coherent integration method even under low SNR conditions. [ABSTRACT FROM AUTHOR]

Proper detection and estimation of signal and noise power measurements are important to generate the best quality of meteorological data products such as Reflectivity, Velocity and Spectrum width. In order to obtain the best quality radar products, it is desirable to compute meteorological parameters by estimating noise power and removal of ground clutter. This paper attempts to study the Empirical Mode Decomposition (EMD) denoising techniques on weather radar signals in the presence of noise and ground clutter. Different methods of EMD based denoising techniques have been considered and applied to the weather signals to check the best performance of the denoising and clutter removal technique. The limitations of these methods are brought out through simulation analysis. To overcome these limitations, this paper proposes a new method for denoising and clutter removal in weather signals. This method is a modified version of the correlation-based EMD Interval Threshold (IT) measurements. Moments have been estimated from these techniques and compared with conventional methods like Pulse pair techniques. [ABSTRACT FROM AUTHOR]

This paper focuses on the 3D modeling of the interior spaces of buildings. Three-dimensional point clouds from laser scanners can be considered the most widely used data for 3D indoor modeling. Therefore, the walls, ceiling and floor are extracted as the main structural fabric and reconstructed. In this paper, a method is presented to tackle the problems related to the data including obstruction, clutter and noise. This method reconstructs indoor space in a model-driven approach using watertight predefined models. Employing the two-step implementation of this process, the algorithm is able to model non-rectangular spaces with an even number of sides. Afterwards, an "improvement" process increases the level of details by modeling the intrusion and protrusion of the model. The 3D model is formed by extrusion from 2D to 3D. The proposed model-driven algorithm is evaluated with four benchmark real-world datasets. The efficacy of the proposed method is proved by the range of [77%, 95%], [85%, 97%] and [1.7 cm, 2.4 cm] values of completeness, correctness and geometric accuracy, respectively. [ABSTRACT FROM AUTHOR]

On Jupiter's largest moon Ganymede, we focus on the southern region of Nippur Sulcus, including the Epigeus palimpsest crater. This region displays geologically recent terrains and has good coverage of optical and near infrared remote sensing data, primarily returned by the NASA Galileo mission. We first use high‐resolution optical imagery to carry out a detailed geological mapping. We combine hyperspectral data to infer the surface composition of this area. We then apply two theoretical models. The first is a self‐similar clustering that uses the mapping of the grooves present in this region to infer the thickness of the ice shell. A second model is used to estimate the radar clutter noise induced by the estimated local topography, which is key for constraining future radar measurements. Based on all findings, we provide a possible evolutionary history of the Nippur Sulcus and Epigeus areas. This exercise shows the potential of an interdisciplinary data analysis combining wide‐ranging scientific objectives such as geology, surface composition, and geophysics in view of the future close exploration that will be conducted on Ganymede by the JUICE spacecraft. Our work combines multiple analysis techniques that could be applied to other regions of interest and will prove crucial when JUICE data become available. Plain Language Summary: On Jupiter's largest moon Ganymede, we focus on the region of Nippur Sulcus, including the Epigeus palimpsest crater covered by optical and near infrared remote sensing data acquired in the past by the NASA Galileo mission, to carry out a detailed geological and compositional analysis of this region. We then apply two theoretical models focusing on the estimation of the grooves' depth and on the radar clutter noise induced by the local topography to show how an in‐depth analysis combining geology, surface composition, and geophysics could achieve a broader understanding of a given region of interest. For high‐priority regions of interest to be studied by JUICE in detail, such a multidisciplinary analysis could ultimately reveal connections between the surface and fluid subsurface. Key Points: We focus on the Nippur Sulcus region on Ganymede to perform a detailed geological and compositional analysis of this regionNippur's grooves have a composition similar to that of small fresh craters and may penetrate 125–135 km into the icy crustTo reveal potential links between the surface and subsurface liquid pockets, clutter‐induced noise must be treated adequately in radar data [ABSTRACT FROM AUTHOR]

The goal of this paper is to improve the detection performance and the false alarm regulation of the conventional order statistics Constant False Alarm Rate (OSCFAR) detectors in a non-homogeneous Gaussian environment. To this end, we design and study the New Sorting Weighting (NSW-) and the Modified Sorting Weighting (MSW-) CFAR detectors. We find closed forms of the detection (Pd) and the false alarm (Pfa) probabilities for both detectors. Moreover, we identify the optimum pairs of weights that maximize the Pd and ensure a constant Pfa. Finally, we prove through Monte Carlo simulations that these detectors provide better detection performance and false alarm regulation than the order statistics conventional ones in various clutter situations with the NSW-CFAR detector being the best one. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) is an important method of clutter suppression that requires adequate training samples. For an airborne conformal array radar, conventional STAP methods do not have enough training samples to acquire good performance due to the range dependent clutter caused by geometry and the problem of polarization. Sparse-recovery-based STAP (SR-STAP) methods have garnered significant attention in the past few decades because they only require a small number of training samples. Sparse Bayesian Learning (SBL) methods have seen increasing amounts of development due to its robust, self-regularizing nature and because it is not sensitive to user parameters, but it converges slowly. In this paper, a novel fast SBL (NFSBL) method is put forward to increase the rate of convergence. To minimize the SBL penalty function, the proposed method introduces the conjugate function to construct a surrogate function. Additional solution sparsity will be achieved through iteratively minimizing the surrogate function. Then, from the proposed method, we could obtain a more accurate clutter plus noise covariance matrix. Numerical simulation results express that this method could acquire better performance of STAP and improvement in convergence and computational complexity for a conformal array. [ABSTRACT FROM AUTHOR]

Through-wall imaging is capable of detecting various living and non-living things behind the wall. The characteristics of the wall under the investigation, amount of clutter and noise govern the quality and reliability of the image as well as the detection ability of the targets using through the wall imaging system. The characteristics of the wall are not known prior, in the literature only the intensity profile is investigated for the unknown wall characteristics using a single dielectric target and the effect of the wall characteristics on the contrast imaging and impact on time or frequency domain features are not investigated. The target with less dielectric is having less reflectivity; hence its detection in the presence of a high reflective target and a noisy environment becomes difficult. In this paper, to enhance the detection ability of the imaging system attenuation constant (α) of the wall is estimated with the proposed wall parameter estimation methods and used as a normalizing factor. To achieve effective beamforming different weighting strategies are developed and the obtained images are compared with the traditional beamforming. Furthermore, a novel approach to finding the effective rank in the low-rank estimation using a statistical model and multi-objective genetic algorithm is proposed for de-noising. [ABSTRACT FROM AUTHOR]

Background: Pixel-based beamforming realizes dynamic focusing at the pixel level with a focused beam by assuming that the received signals are composed of spherical pulses. Far-focused pixel-based (FPB) imaging was proposed to avoid artifacts around the focal depth. However, the contrast improvement is limited.Objective: We propose an adaptive weighting method based on dynamic phase coherence factor (DPCF) to improve the image contrast while preserving the speckle pattern.Methods: The phase variation is dynamically estimated based on the noise energy proportion of echo signals and it is used to calculate phase coherence weights for suppressing interference and preserving desired signals. A depth-dependent parameter is designed for DPCF to enhance the performance of noise and clutter suppression in the far-field region. We further use the subarray averaging technique to smooth the speckle texture.Results: The proposed method was evaluated on simulated, phantom experimental, and in vivo data. Results show that, compared with the phase coherence factor (PCF) based method, DPCF respectively leads to average CR improvements by more than 60% and 24% in simulation and experiment, while obtaining an improved speckle signal-to-noise ratio.Conclusions: The proposed method is a potentially valuable approach to obtaining high-quality ultrasound images in clinical applications. [ABSTRACT FROM AUTHOR]

Due to the restriction of the low-resolution systems and the interference of background clutter and environmental noise in the exploration process, the traditional classification and recognition algorithms of conventional radar for aircraft targets have low accuracy and poor feature stability. To solve the above problems, this paper proposes to apply high-order cumulant spectrum and deep convolutional neural network (CNN) to feature the extraction and classification of aircraft target radar echoes. Firstly, analyze the high-order statistical characteristics of aircraft echoes, calculate their bispectrum, and then enhance the generated bispectrum dataset. Finally, use the augmented dataset to train and test the deep CNN, and obtain the final classification and recognition results. Experimental results show that the proposed method can accurately classify and identify multiple aircraft targets in the dataset, indicating that the bispectral features can better reflect the target characteristics, and the classification method combined with the deep learning model has good classification and identification performance and noise robustness. [ABSTRACT FROM AUTHOR]

Active Radar Calibrators (ARC), also called calibration transponders, are often used as reference targets for absolute radiometric calibration of radar systems due to their large achievable Radar Cross-Section (RCS). However, before using a transponder as a reference target, the hardware itself has to be calibrated. A novel method, called the three-transponder method, was proposed some years ago and allows for RCS calibration of digital transponders without using any RCS targets as reference. In this paper, this technique is further refined and applied to a setup utilizing only one digital transponder. The accurate measurement design is described and a novel, elaborated data processing scheme is developed to minimize remaining noise and clutter effects in the data. A comprehensive error analysis is presented in the second part of this paper. [ABSTRACT FROM AUTHOR]

The temporal variation of received clutter and noise at a pair of oceanographic high frequency radars (HFR) operating near the geomagnetic equator in the Republic of Palau is investigated. Oceanographic HFRs process range-gated Doppler spectra from groundwave signals that are backscattered from the ocean's surface to derive maps of ocean currents. The range performance of the radars exhibited a regular diurnal signal which is determined to be a result of both ionospheric clutter and noise. The increased Clutter plus Noise Floor (C+NF) decreases the Signal to Clutter plus Noise Ratio (SCNR) which, in turn, reduces the range and quality of ocean surface current measurement. Determining the nature and origin of this degradation is critical to QA/QC of existing HFR deployments as well as performance predictions of future installations. Nighttime impacts are most severe and negatively affect ocean surface current measurements as low SCNR is found to extend across the Doppler spectra at all ranges, challenging the ability of HFR to map the ocean surface current. Daytime degradation is less severe and presents itself in a way consistent with independent observations of ionospheric clutter, specifically the diurnal temporal pattern and range where the C+NF features occur. A timeseries analysis of SCNR and C+NF is pursued to understand this relationship using received range-dependent Doppler spectra and C+NF features using image segmentation techniques. Clutter plus noise features are classified into daytime, nighttime, and no-noise feature types. The diurnal structure and variability of these features are examined, and the occurrences of each feature type are calculated. The occurrences are compared with space weather indices including a measure of geomagnetic activity, namely the EE (Equatorial Electro Jet) index (determined from magnetometers measuring the earth's magnetic field), as well as solar impacts using the F10.7 solar radio clutter index to assess the relationship of ionospheric conditions with HFR ocean surface current measurement. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. Infrared point-target detection is one of the key technologies in infrared guidance systems. Due to the long observation distance, the point target is often submerged in the background clutter and large noise in the process of atmospheric transmission and scattering, and the signal-to-noise ratio is low. On the other hand, the target in the image appears in the form of fuzzy points, so that the target has no obvious features and texture information. Therefore, scholars have proposed many object detection methods for dimming infrared images, which has become a hot research topic on account of the flow-rank model based on the image patch. However, the result has a high false alarm rate because the most low-rank models based on the image patch do not consider the spatial-temporal characteristics of the infrared sequences. Therefore, we introduce 3D total variation (3D-TV) to regularize the foreground on account of the non-convex rank approximation minimization method, so as to consider the spatial-temporal continuity of the target and effectively suppress the interference caused by dynamic background and target movement on the foreground extraction. Finally, this paper proposes the minimization of the non-convex spatial-temporal tensor low-rank approximation algorithm (MNSTLA) by studying the related algorithms of the point infrared target detection, and the experimental results show strong robustness and a low false alarm rate for the proposed method compared with other advanced algorithms, such as NARM, RIPT, and WSNMSTIPT. [ABSTRACT FROM AUTHOR]

Document image analysis and processing has drawn the attention of many researchers due to its real-time applications in day-to-day life. Document database comprising of logo provides a good opportunity for an easier way of indexing, searching and retrieval of the documents. Logo detection is an essential need for the implementation of any logo-based document indexing or retrieval techniques. This paper aims to develop an efficient logo detection method for document images. The major steps employed in the developed system include preprocessing of the input document, finding the connected components and classification of these components into the logo and non-logo candidates. The preprocessing step employs a median filter and a unique procedure for the removal of clutter noise to reduce the false detection rate. Histogram of Oriented Gradient (HOG) features and an SVM classifier are used to identify the logo and non-logo candidates of the document. The presented system is evaluated using Tobacco 800 dataset and the results are compared with existing techniques. The results show an improvement of 5% in average logo detection rate with the proposed work. [ABSTRACT FROM AUTHOR]

Small target detection under a complex background has always been a hot and difficult problem in the field of image processing. Due to the factors such as a complex background and a low signal-to-noise ratio, the existing methods cannot robustly detect targets submerged in strong clutter and noise. In this paper, a local gradient contrast method (LGCM) is proposed. Firstly, the optimal scale for each pixel is obtained by calculating a multiscale salient map. Then, a subblock-based local gradient measure is designed; it can suppress strong clutter interference and pixel-sized noise simultaneously. Thirdly, the subblock-based local gradient measure and the salient map are utilized to construct the LGCM. Finally, an adaptive threshold is employed to extract the final detection result. Experimental results on six datasets demonstrate that the proposed method can discard clutters and yield superior results compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, a new space-time adaptive processing (STAP) method based on improved nested arrays and pulses configurations is proposed. Specifically, we first decompose the sensor array into two uniform linear arrays (ULAs) plus a separate sensor, similarly for pulse trains. Then, the original received signals from the physical array and pulse trains are introduced into the virtual domain, where the virtual clutter plus noise covariance matrix (CNCM) estimation is performed. Since the system has more virtual sensors and pulses from the perspective of virtual domain, the degrees of freedom (DOF) capability is effectively enhanced to improve the angle and Doppler resolution of radar. With the spatial-temporal smoothing technique, the STAP filter is designed by reconstructing the CNCM and virtual signal steering vector. Simulation results validate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Power-Doppler ultrasonic (PD-US) imaging is sensitive to echoes from blood cell motion in the microvasculature but generally nonspecific because of difficulties with filtering nonblood-echo sources. We are studying the potential for using PD-US imaging for routine assessments of peripheral blood perfusion without contrast media. The strategy developed is based on an experimentally verified computational model of tissue perfusion that simulates typical in vivo conditions. The model considers directed and diffuse blood perfusion states in a field of moving clutter and noise. A spatial registration method is applied to minimize tissue motion prior to clutter and noise filtering. The results show that in-plane clutter motion is effectively minimized. While out-of-plane motion remains a strong source of clutter-filter leakage, those registration errors are readily minimized by straightforward modification of scanning techniques and spatial averaging. [ABSTRACT FROM AUTHOR]

In recent years, non-contact human–computer interactions have aroused much attention. In this paper, we mainly propose a dual view observation system based on the frontal and side millimeter-wave radars (MWR) to collect echo data of the Air writing digits "0~9", simultaneously. Additionally, we also propose a novel distance approximation method to make the trajectory reconstruction more efficient. To exploit these characteristics of spatial-temporal adjacency in handwriting digits, we propose a novel clustering algorithm, named the constrained density-based spatial clustering of application with noise (CDBSCAN), to remove background noise or clutter. Moreover, we also design a robust gesture segmentation method by using twice-difference and high–low thresholds. In our trials and comparisons, based on the trajectories formulated by echo data series of time–distance and time–velocity of dual views, we present a lightweight-based convolution neural network (CNN) to realize these digits recognition. Experiment results show that our system has a relatively high recognition accuracy, which would provide a feasible application for future human–computer interaction scenarios. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is one of the most generally used underground sensing equipment, but it is frequently contaminated by clutter and noise during data acquisition, which has a significant impact on the detection performance of buried targets. The purpose of this letter is to present a novel clutter suppression method based on the principal component Gaussian curvature decomposition (PCGCD). First, the GPR B-scan data are divided into different sub-components using principal component analysis (PCA). Then, a Gaussian curvature decomposition (GCD) method is proposed, which can be applied to PCA domain subspaces to recover more target structure information from random noise. The PCGCD method's performance is evaluated using both numerical simulation and real-world GPR datasets. The visualization and quantitative results demonstrated our method's superiority in protecting the underground target structure, removing complex random noise, and improving the detection ability of buried targets. [ABSTRACT FROM AUTHOR]