Clutter Suppression Research Articles

Precision Detection of Infrared Small Target in Ground-to-Air Scene

Reliable infrared small target detection plays an important role in infrared search and track systems. In recent years, most target detection methods usually use the statistical features of a rectangular window to represent the contrast between the target and the background. When the size of the target is small or the target is close to the background, the statistical features of the rectangular window would reduce the significance of the target. Moreover, such methods have limited effect on interfering targets, high brightness background, background edges, and clutter suppression in complex backgrounds, and are likely to misdetect the target or even miss it. This paper proposes a non-window, structured algorithm for precision detection of infrared small targets under ground-to-air complex scenes. The non-window, structured local grayscale descent intensity and local gradient watershed (LGDI-LGW) filter can detect a 1 × 1 pixel infrared small target, and effectively suppress interfering targets and background edges. By using the adaptive threshold and centroid algorithm on the target area, the precision of target coordinates reaches sub-pixel accuracy. The results of 9 simulation experiments show that the algorithm has the lowest false alarm rate and the highest detection rate compared with the eight baseline algorithms. It can effectively detect targets with Gaussian distribution of grayscale values and targets with grayscale values approximating tree stump structure. The results of 2 engineering experiments show that under simulated near-sun conditions, a uniform target is precisely detected, and the UAV point target is precisely detected in complex ground-to-air scenes.

Association of renal circulation at acute phase with decongestion level at discharge in patients admitted for acute decompensated heart failure

Abstract Background In patients admitted with acute decompensated heart failure (ADHF), renal hemodynamics may play an important role in the clinical course during hospitalization based on the close cardio-renal relationship. Superb Microvascular Imaging (SMI), distinguished by its advanced clutter suppression capabilities to reveal microflow details, holds promise for evaluating renal circulation. Consequently, our study aims to explore the association between renal circulation as assessed by SMI and the levels of brain natriuretic peptide (BNP) at discharge as an indicator of decongestion status. Methods In a prospective design, 45 ADHF patients were sequentially examined via renal ultrasound with SMI at discharge. The vascular index (VI) by SMI was calculated as the percentage of blood flow signal area in the region of interest. Then, the intra-renal perfusion index (IRPI) as an index of cyclic variation in VI, was calculated as the maximum VI minus the minimum VI within one cardiac cycle in the ROI divided by the Max.VI (Figure A). Results The values of IRPI were as follows: at 12h, 0.70±0.04; at 48h, 0.58±0.05; at 1W, 0.50±0.04; and at discharge, 0.61±0.04. The IRPI measured at 48h showed a stronger correlation with BNP at discharge (r = 0.57, p<0.001) than those at the other three points (Figure B). Patients with higher log BNP at discharge (> 2.31: determined by the median value) had significantly higher IRPI and age, and lower hemoglobin levels at 48h, however, there were no significant differences in estimated glomerular filtration rate, ejection fraction, or systolic pulmonary artery pressure at 48h. The area under the curve of IRPI at 48h for higher log BNP at discharge > 2.31 at discharge was 0.746 (sensitivity 100%, specificity 48%). Conclusion Early assessment of renal circulation by SMI around 48 hours after ADHF admission may predict congestion resolution at discharge. This underscores the potential utility of SMI in guiding more aggressive decongestive treatment strategies earlier in ADHF.

Millimeter-Wave Radar Clutter Suppression Based on Cycle-Consistency Generative Adversarial Network

Vehicle-mounted millimeter-wave radar is widely used in autonomous driving systems for its ability to observe road scenes at all times and in all weathers. However, the data collected by millimeter-wave radar are seriously affected by the existence of clutter. This clutter will result in false detection during object detection. To address this issue, a feature extraction network with clutter suppression is necessary. This paper proposes a new clutter suppression method for millimeter-wave Range–Angle (RA) images based on a cycle-consistency generative adversarial network (CycleGAN). The generator of the method can be used as the feature extraction network of the object detection. The method aims to convert cluttered images into clutter-free images by unsupervised learning. In this method, an attention gate (AG) is introduced into the generator, a spatial attention mechanism that improves the ability of the model to automatically learn to focus on the features of targets and suppress the clutter of the background. Additionally, the target consistency loss term is added to the loss function to maintain target integrity while suppressing network training overfitting. The public dataset CRUW is utilized to evaluate the performance of the proposed method, which is compared and analyzed with traditional methods and deep learning methods. Experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the proposed method reach 39.846 and 0.990, respectively.

Lightweight Multi-Domain Fusion Model for Through-Wall Human Activity Recognition Using IR-UWB Radar

Impulse radio ultra-wideband (IR-UWB) radar, operating in the low-frequency band, can penetrate walls and utilize its high range resolution to recognize different human activities. Complex deep neural networks have demonstrated significant performance advantages in classifying radar spectrograms of various actions, but at the cost of a substantial computational overhead. In response, this paper proposes a lightweight model named TG2-CAFNet. First, clutter suppression and time–frequency analysis are used to obtain range–time and micro-Doppler feature maps of human activities. Then, leveraging GhostV2 convolution, a lightweight feature extraction module, TG2, suitable for radar spectrograms is constructed. Using a parallel structure, the features of the two spectrograms are extracted separately. Finally, to further explore the correlation between the two spectrograms and enhance the feature representation capabilities, an improved nonlinear fusion method called coordinate attention fusion (CAF) is proposed based on attention feature fusion (AFF). This method extends the adaptive weighting fusion of AFF to a spatial distribution, effectively capturing the subtle spatial relationships between the two radar spectrograms. Experiments showed that the proposed method achieved a high degree of model lightweightness, while also achieving a recognition accuracy of 99.1%.

Range-Ambiguous Clutter Suppression for Space-Based Early Warning Radar via EPC–FDA–MIMO With Nonorthogonal Waveforms

Range-Ambiguous Clutter Suppression for Space-Based Early Warning Radar via EPC–FDA–MIMO With Nonorthogonal Waveforms

Ground penetrating radar clutter suppression based on the weighted tensor Schatten p-paradigm minimization of tensor principal components

The issue of clutter suppression in ground-penetrating radar (GPR) detection has consistently been a prominent area of research. The existing tensor robust principal component analysis (TRPCA) approach encounters a challenge when handling different singular values, whereby all singular values are reduced to the same degree, leading to loss of information in the low-rank part. To address the aforementioned issues, this study developed a TRPCA method based on weighted tensor Schatten p-paradigm minimisation (p-TRPCA). This approach assigns distinct weights to each singular value by using a weighted tensor Schatten p-paradigm, facilitating the optimal decomposition of GPR data into low-rank clutter and sparse target components. The processing of target echoes is then completed by applying a threshold judgement to the results. The efficacy of the proposed method was validated through simulations and real-world testing. Its performance was benchmarked against established advanced clutter removal techniques in terms of peak signal-to-noise ratio (PSNR) and signal-to-clutter ratio. The findings demonstrated that the method effectively delineated the low-rank clutter matrix and sparse target matrix within the B-scan image and exhibited superior SNR compared to other methods.

Isolated point clutter suppression method for airborne STAP radar in wind farm environment

With the large-scale construction of wind farms, wind turbine isolated point clutter has an increasingly serious impact on airborne radar target detection performance. Traditional space-time adaptive processing methods cannot suppress wind turbine clutter (WTC) with spectrum broadening characteristics, which may lead to a decrease in target detection probability and an increase in false alarm rate. In this paper, a WTC suppression method for airborne radar based on micro-Doppler features is proposed, and we construct the feature subspace of wind turbine echo to distinguish wind turbine, target, clutter, and noise. First, the Sobel operator is used to process the radar range-Doppler spectrum, and the range cells of the wind turbines are preliminarily judged. Then the smallest of constant false alarm rate (SOCFAR) method is used to further confirm the range cells where the wind turbines are located. Next, Mahalanobis distance is used to estimate the optimal dictionary atomic parameters of WTC, and the updated dictionary atoms are used to construct an orthogonal projection matrix to suppress WTC. Finally, short-range nonstationary clutter and sidelobe clutter are suppressed by space-time adaptive segment processing. On the one hand, the proposed method realizes the accurate positioning of wind turbines through image edge detection and constant false alarm detection. On the other hand, Mahalanobis distance is used to estimate the atomic parameters of the wind turbine dictionary, which ensures the homogeneity of wind turbine samples after clutter suppression. The simulation and measured data results show that the proposed method can significantly reduce the false alarm rate caused by WTC while ensuring the effective detection of the target.

Noise suppression in pulsed IR thermographic NDT: Efficiency of data processing algorithms

Various types of noise, which accompany active TNDT procedures using optical heating, have been analyzed, both numerically and experimentally. An emphasis has been made on the suppression of surface clutter, which represents local areas of varying absorptivity/emissivity. The concept of signal-to-noise that is typically used in defect detection has been applied to fixed pattern noise in order to compare capabilities of data processing algorithms in reducing surface clutter. The experimental investigation has been fulfilled on a special sample containing both subsurface air-filled defects and areas with varying emissivity/absorptivity. The best suppression of the fixed pattern noise was provided by the complex wavelet transform and principle component analysis. Because of 3D heat diffusion, clutter spot boundaries are often underlined by particular data processing algorithms thus producing specific contours. The test situations where subsurface defects are located under localized clutter spots have been analyzed to demonstrate an overshadowing effect of such spots when detecting hidden defects.

Reduced-Dimensional STAP Method for Nonstationary Clutter Suppression in Endfire Array Airborne Radar

Reduced-Dimensional STAP Method for Nonstationary Clutter Suppression in Endfire Array Airborne Radar

Research on clutter suppression method based on velocity distribution in vegetation clutter environment

Research on clutter suppression method based on velocity distribution in vegetation clutter environment

Cascade Clutter Suppression Method for Airborne Frequency Diversity Array Radar Based on Elevation Oblique Subspace Projection and Azimuth-Doppler Space-Time Adaptive Processing

Cascade Clutter Suppression Method for Airborne Frequency Diversity Array Radar Based on Elevation Oblique Subspace Projection and Azimuth-Doppler Space-Time Adaptive Processing

Motion Clutter Suppression for Non-Cooperative Target Identification Based on Frequency Correlation Dual-SVD Reconstruction.

Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target's micro-motion component and the energy entropy of the time-frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data.

Clutter Rank Estimation Method for Bistatic Radar Systems Based on Prolate Spheroidal Wave Functions

Bistatic radar exhibits spatial isomerism and diverse configurations, leading to unique clutter characteristics distinct from those of monostatic radar. The clutter rank serves as a pivotal indicator of clutter characteristics, enabling the quantification of clutter severity. Space-time adaptive processing (STAP) is a critical technique to detect moving targets, and clutter rank determines the number of independent and identically distributed (IID) training samples and the degree of freedom (DOF) for effective suppression of clutter that STAP requires. Therefore, the accurate estimation of clutter rank for bistatic radar can provide a crucial indicator for designing and constructing STAP processors, thereby facilitating fast and efficient clutter suppression in bistatic radar systems. This study is based on the idea that clutter rank is the number of prolate spheroidal wave function (PSWF) orthogonal bases utilized for approximating the clutter signal. Firstly, the challenge of utilizing PSWF orthogonal bases for approximating the clutter signal in bistatic radar is elucidated. This pertains to the fact that, unlike monostatic radar clutter, bistatic radar clutter is not capable of being expressed as a single-frequency signal. The clutter rank estimation for bistatic radar is thus derived as the frequency bandwidth estimation. Secondly, to achieve this estimation, the frequency distribution of each individual scattering unit is investigated, thereby determining their extending frequency broadening (EFB) as compared to that of single-frequency. Subsequently, the integral average of EFB across the entire range bin is computed, ultimately enabling the acquisition of bistatic radar’s frequency bandwidth. Finally, the estimation method is extended to non-side-looking mode and limited observation areas with pattern modulation. Simulation experiments confirm that our proposed method provides accurate clutter rank estimations, surpassing 99% proportions of large eigenvalues across various bistatic configurations, observation modes, and areas.

MVDR Algorithm Based on Loading Factor and Sample Selection Strategy

The MVDR (Minimum Variance Distortionless Response) algorithm is a classic Wiener filtering method used for beamforming in array signal processing. In a one-dimensional linear array high-frequency ground wave radar system, it can be employed to suppress various types of ionospheric clutter. A key step in suppression is the use of maximum likelihood estimation (MLE) to estimate the ionospheric clutter covariance matrix. However, MLE typically assumes that samples are independently and identically distributed (i.i.d.). Traditional MVDR algorithms estimate the clutter covariance matrix using all samples, which may not satisfy the i.i.d. condition. Therefore, this paper proposes a new sample selection strategy that utilizes the Mahalanobis distance method to select samples. Meanwhile, due to the very small numerical values of the ionospheric clutter covariance matrix, even if the matrix is full-rank, numerical instability may occur during inversion. To address this issue, the paper introduces 4 different forms of regularization factors. Empirical data demonstrate that the proposed new method offers better suppression of ionospheric clutter compared to the traditional MVDR algorithm.

Sub-Aperture Synthetic Aperture Radar Imaging of Fixed-Platform Beam-Steering Radar for Blast Furnace Burden Surface Detection.

Due to the scheme of fixed-platform beam-steering radar and the space of the blast furnace being subjected to harsh environmental influences, the traditional detection methods of burden surface are limited by geometric distortion, noncoherent clutter, and noise interference, which leads to an increase in the image entropy value and the equivalent number of views, makes the density distribution of burden surface show a diffuse state, and greatly affects the stability and accuracy. In this paper, a new fixed-platform beam-steering radar synthetic aperture radar imaging method (FPBS-SAR) is proposed in the sensory domain of the blast furnace environment. From the perspective of fixed-platform beam-steering radar motion characteristics, the target range-azimuth coupled distance history model under the sub-aperture is established, the azimuthal Doppler variation characteristics of the fixed-platform beam-steering process are analyzed, and the compensation function of the transform domain for geometric disturbance correction is proposed. For noncoherent noise suppression in blast furnaces, the trimmed geometric mean-order-likelihood CFAR method is proposed to take into account the information of burden surface and clutter suppression. To verify the method, point target simulation and imaging for the industrial field measurement data are carried out. The results indicate that geometric distortion is well eliminated, the image entropy value and the equivalent number of views have decreased, and noncoherent noise in blast furnaces is suppressed.

An Efficient Sparse Recovery STAP Algorithm for Airborne Bistatic Radars Based on Atomic Selection under the Bayesian Framework

The traditional sparse recovery (SR) space-time adaptive processing (STAP) algorithms are greatly affected by grid mismatch, leading to poor performance in airborne bistatic radar clutter suppression. In order to address this issue, this paper proposes an SR STAP algorithm for airborne bistatic radars based on atomic selection under the Bayesian framework. This method adopts the idea of atomic selection for the process of Bayesian inference, continuously evaluating the contribution of atoms to the likelihood function to add or remove atoms, and then using the selected atoms to estimate the clutter support subspace and perform sparse recovery in the clutter support subspace. Due to the inherent sparsity of clutter signals, performing sparse recovery in the clutter support subspace avoids using a massive number of atoms from an overcomplete space-time dictionary, thereby greatly improving computational efficiency. In airborne bistatic radar scenarios where significant grid mismatch exists, this method can mitigate the performance degradation caused by grid mismatch by encrypting grid points. Since the sparse recovery is performed in the clutter support subspace, encrypting grid points does not lead to excessive computational burden. Additionally, this method integrates out the noise term under a new hierarchical Bayesian model, preventing the adverse effects caused by inaccurate noise power estimation during iterations in the traditional SR STAP algorithms, further enhancing its performance. Our simulation results demonstrate the high efficiency and superior clutter suppression performance and target detection performance of this method.

Sea clutter suppression using neural network

Sea clutter suppression is a critical task in radar systems to enhance target detection performance in complex naval environments and at coastlines. This paper discusses the use of neural networks for marine clutter suppression and coastal surveillance radar clutter suppression. Effective maritime clutter suppression is made possible by the Feed Forward Neural Network (FFNN) and Principal Component Analysis (PCA) based clutter reduction method, which uses neural network deep learning capabilities to automatically identify and extract features and patterns from raw radar data. Support Vector Machine (SVM) is utilized for clutter suppression along the shoreline. To train and test the network model, a sizable collection of radar measurements, including clutter and target echoes, is gathered. After pre-processing, the gathered data is used in a specially created model, which uses its underlying patterns to distinguish between target echoes and clutter. Then, clutter in real-time radar signals is suppressed using the learned neural network models, improving the detection of targets on the sea and at the coastline. Performance measures Structural Similarity (SSIM) and Signal to Noise Ratio (SNR) shows that the proposed method provides improved clutter reduction. Highlights Maritime clutter suppression is required for better visibility of targets Neural Networks provides better clutter suppression Clutter reduction using PPI images helps in Surveillance applications Objective performance measures SNR and SSI are used for performance analysis Better training and good dataset helps in improved clutter reduction for neural network approaches

A Data and Model-Driven Clutter Suppression Method for Airborne Bistatic Radar Based on Deep Unfolding

Space–time adaptive processing (STAP) based on sparse recovery achieves excellent clutter suppression and target detection performance, even with a limited number of available training samples. However, most of these methods face performance degradation due to grid mismatch, which impedes their application in bistatic clutter suppression. Some gridless methods, such as atomic norm minimization (ANM), can effectively address grid mismatch issues, yet they are sensitive to parameter settings and array errors. In this article, the authors propose a data and model-driven algorithm that unfolds the iterative process of atomic norm minimization into a deep network. This approach establishes a concrete and systematic link between iterative algorithms, extensively utilized in signal processing, and deep neural networks. This methodology not only addresses the challenges associated with parameter settings in traditional optimization algorithms, but also mitigates the lack of interpretability issues commonly found in deep neural networks. Moreover, due to more rational parameter settings, the proposed algorithm achieves effective clutter suppression with fewer iterations, thereby reducing computational time. Finally, extensive simulation experiments demonstrate the effectiveness of the proposed algorithm in clutter suppression for airborne bistatic radar.

Spreading Sea Clutter Suppression for High-Frequency Hybrid Sky-Surface Wave Radar Using Orthogonal Projection in Spatial–Temporal Domain

In recent years, the high-frequency hybrid sky-surface wave radar (HSSWR) has been increasingly used for target detection applications. Nevertheless, the specific bistatic system layout and the phase path disturbances induced by the ionospheric propagation channel may severely spread the sea clutter spectrum, thereby deteriorating the detection ability of the HSSWR for slow-moving targets. In this work, a novel subspace method based on the hybrid use of the amplitude and phase estimator (APES) and the orthogonal projection (OP) in the spatial–temporal domain, denoted as the APES-OP method, is proposed to suppress the spreading first-order sea clutter of the HSSWR. The distribution characteristics of targets and first-order sea clutter in the spatial–temporal domain were investigated, and a time-domain subspace signal model was adopted to describe targets perturbed by ionospheric phase path modulation. An APES filter was adopted to filter out the potential targets with a traversal approach to avoid attenuating desired signals while suppressing sea clutter. After that, sampling data from multi-channels and slow-time domains at the cell under test were employed to construct a spatial–temporal matrix, which was then utilized to obtain the sea clutter subspace by singular value decomposition. Simulation results indicate that the proposed algorithm can suppress sea clutter while retaining the target, even if the target is buried by sea clutter. The processing results of measured data further demonstrate the efficiency of the proposed algorithm. After sea clutter suppression, the target obscured by clutter can be revealed, and the signal-to-clutter ratio of the target is greatly improved.

Clutter Mitigation for Wind Profiler Radar Using Adaptive Clutter Suppression and Median Filtering for Doppler Spectra

Clutter Mitigation for Wind Profiler Radar Using Adaptive Clutter Suppression and Median Filtering for Doppler Spectra