Radar Clutter Research Articles

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.

Joint Prediction of Sea Clutter Amplitude Distribution Based on a One-Dimensional Convolutional Neural Network with Multi-Task Learning

Accurate modeling of sea clutter amplitude distribution plays a crucial role in enhancing the performance of marine radar. Due to variations in radar system parameters and oceanic environmental factors, sea clutter amplitude distribution exhibits multiple distribution types. Focusing solely on a single type of amplitude prediction lacks the necessary flexibility in practical applications. Therefore, based on the measured X-band radar sea clutter data from Yantai, China in 2022, this paper proposes a multi-task one-dimensional convolutional neural network (MT1DCNN) and designs a dedicated input feature set for the joint prediction of the type and parameters of sea clutter amplitude distribution. The results indicate that the MT1DCNN model achieves an F1 score of 97.4% for classifying sea clutter amplitude distribution types under HH polarization and a root-mean-square error (RMSE) of 0.746 for amplitude distribution parameter prediction. Under VV polarization, the F1 score is 96.74% and the RMSE is 1.071. By learning the associations between sea clutter amplitude distribution types and parameters, the model’s predictions become more accurate and reliable, providing significant technical support for maritime target detection.

Performance analysis of mean level CFAR detectors in homogeneous gamma-distributed radar clutter

ABSTRACT In this paper, a novel and exact mathematical formulation of three mean level constant false alarm rate (CFAR) detectors has been developed considering a homogeneous gamma-distributed (GM) clutter. These detectors include the cell averaging (CA), the greatest-of (GO), and the smallest-of (SO)-CFAR detection schemes. To do so, we derived closed-form expressions for the probability density function (pdf), and the cumulative distribution function (cdf) of the sum of an exponential fluctuating target submerged in gamma-distributed sea clutter. Then, we developed exact and explicit expressions for the probability of false alarm (P fa ) and probability of detection (P d ) of the optimal fixed threshold detector and the three considered CFAR detectors. The correctness of our expressions is then examined and validated via numerical simulations. Assuming a homogeneous GM background, the detection performance of these three detectors is carried out, discussed, and compared with that of the optimal detector. Experimental results with Sentinel-1 synthetic aperture radar (SAR) data demonstrate the effectiveness of the proposed closed-form expressions and show that the considered CFAR schemes are efficient for ship detection in sea clutter backgrounds.

Marine Radar Constant False Alarm Rate Detection in Generalized Extreme Value Distribution Based on Space-Time Adaptive Filtering Clutter Statistical Analysis

The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection performance and the modeling problem of non-uniform, non-Gaussian, and non-stationary sea clutter distribution in marine radar images, in this paper, a CFAR detection method in generalized extreme value distribution modeling based on marine radar space-time filtering background clutter is proposed. Initially, three-dimensional (3D) frequency wave-number (space-time) domain adaptive filter is employed to filter the original radar image, so as to obtain uniform and stable background clutter. Subsequently, generalized extreme value (GEV) distribution is introduced to integrally model the filtered background clutter. Finally, Inclusion/Exclusion (IE) with the best performance under the GEV distribution is selected as the clutter range profile CFAR (CRP-CFAR) detector decision rule in the final detection. The proposed method is verified by utilizing real marine radar image data. The results indicate that when the Pfa is set at 0.0001, the proposed method exhibits an average improvement in PD of 2.3% compared to STAF-RCBD-CFAR, and a 6.2% improvement compared to STCS-WL-CFAR. When the Pfa is set at 0.001, the proposed method exhibits an average improvement in PD of 6.9% compared to STAF-RCBD-CFAR, and a 9.6% improvement compared to STCS-WL-CFAR.

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.

Improving data-driven estimation of significant wave height through preliminary training on synthetic X-band radar sea clutter imagery

Improving data-driven estimation of significant wave height through preliminary training on synthetic X-band radar sea clutter imagery

Weak Target Detection Based on Full-Polarization Scattering Features under Sea Clutter Background

Aiming at the low observable target detection under sea clutter backgrounds, this paper emphasizes the exploration of distinguishable full-polarization features between target and sea clutter echoes. To overcome the shortcomings of the existing polarization feature-based methods, the full-polarization features of sea clutter are modeled and analyzed in detail by using Van Zyl polarization decomposition. Then, three polarimetric features (the relative surface scattering energy, the relative dihedral scattering energy and the relative diffuse scattering energy) are extracted from the fully polarimetric radar sea clutter echoes, which improve the feature differences between sea clutter and targets. And a tri-polarimetric feature detector with constant false alarm rate (CFAR) is constructed based on the fast convex hull learning algorithm. The experimental results on the real measured IPIX radar datasets prove that the proposed full-polarization feature detector obtains more competitive detection performance and lower computational complexity than the several existing feature-based detectors.

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.

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 Laboratory test aimed at demostrating the potential of an Interferometric Radar to estimate the Seismic Risk of damaged buildings

In the last decade, the use of interferometric Real Aperture Radar (RAR) to perform the survey of static and dynamical performance of civil structures, and estimate their vibration state and modal behaviour, for bridge monitoring, consolidated. In the case of urban buildings, due to the small amplitude of vibration of these structures, and the higher complexity of the radar survey, a few case studies have been published. Nonetheless, recent research [1] focused on the potential of measuring the frequency of vibration of a building after a seismic event to estimate the occurred damage using this non-invasive and remote sensing technique. The study here described reports the results of an experiment carried out to investigate the potential of this application through a laboratory setup. A framed metal structure, composed of bars fasten by bolts, representing a scaled reproduction of a civil building, is monitored using a portable K band (24 GHz) radar interferometer. The instrument is a prototype developed at CTTC which provides improved range resolution and displacement accuracy, with respect to the state of the art of commercial instruments. In the experiment, the structure is monitored by the radar step by step while its stiffness is decreased by loosening, in different points, the bolts constraining the structure. This procedure aim simulating an increasing damage. Simultaneously to the radar acquisitions, data from a setup of accelerometers mounted on the framed structure are also acquired. Despite the high environmental noise (radar clutter) caused by multiple reflections generated by the neighbour walls and furniture elements internal to the laboratory, the spectral analysis of the displacement samples retrieved from radar acquisitions and from the accelerometers confirm the expected behaviour. [1] Gonzalez-Drigo, R. et al., Assessment of Post-Earthquake Damaged Building with Interferometric Real Aperture Radar. Remote Sens. 2019, 11, 2830. https://doi.org/10.3390/rs11232830

An airborne radar sea clutter spectrum parameters estimation method based on intelligent learning

Traditional airborne radar sea clutter suppression methods have a high degree of human participation and large errors in estimating the clutter power spectrum. With the development of modern signal processing and artificial intelligence, deep learning methods are used to study the sea clutter more quickly and intelligently. This paper proposes an airborne radar sea clutter spectrum parameter estimation method based on intelligent learning. It establishes a sea clutter training model based on the one-dimensional LeNet-5. Then the simulated and measured sea clutter data are input into the trained model to estimate the center and width of the power spectrum, thus realizing the direct perception of the sea clutter spectrum characteristics. The experimental results show that the proposed method has a higher estimation accuracy and better robustness than the traditional methods.

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

In cluttered electromagnetic environments, radar is often disturbed by varied clutter, making target detection challenging. Therefore, achieving effective clutter suppression is crucial for radar target detection. However, traditional clutter suppression methods face three key challenges: (1) significant degradation in target signal detection performance when the clutter’s Doppler spectrum completely masks the target signal; (2) heavy reliance on prior knowledge for optimal performance; and (3) inherent signal energy loss during clutter suppression. To address these challenges, we propose a clutter suppression scheme based on blind source separation (BSS). Initially, the scheme utilizes parallel principal skewness analysis (PPSA) to process the echo signals in the range domain, which helps in identifying the position of moving targets. Subsequently, PPSA is applied once more to process the moving targets in the Doppler domain, allowing for the precise determination of their relative velocities. Subsequently, we evaluate the scheme’s performance with simulated and real data, comparing it with traditional clutter suppression methods and other BSS techniques. The results confirm the effectiveness of the scheme in clutter suppression.

A New Extended Target Detection Method Based on the Maximum Eigenvalue of the Hermitian Matrix

In the field of radar target detection, the conventional approach is to employ the range profile energy accumulation method for detecting extended targets. However, this method becomes ineffective when dealing with non-stationary and non-uniform radar clutter scenarios, as well as long-distance targets with weak radar cross sections (RCSs). In such cases, the signal-to-noise ratio (SNR) of the target echo is severely degraded, rendering the energy accumulation detection algorithm unreliable. To address this issue, this paper presents a new extended target detection method based on the maximum eigenvalue of the Hermitian matrix. This method utilizes a detection model that incorporates observed data and employs the likelihood ratio test (LRT) theory to derive the maximum eigenvalue detector at low SNR. Specifically, the detector constructs a matrix using a sliding window block with the available data and then computes the maximum eigenvalue of the covariance matrix. Subsequently, the maximum eigenvalue matrix is transformed into a one-dimensional eigenvalue image, enabling extended target detection through analogy with the energy accumulation detection method. Furthermore, this paper analyzes the proposed extended target detection method from both theoretical and experimental perspectives, validating it through field-measured data. The results obtained from the measured data demonstrate that the method effectively enhances the SNR in low SNR conditions, thereby improving target detection performance. Additionally, the method exhibits robustness across different scattering center targets.

Bayesian inference for amplitude distribution with application to radar clutter

The performance of telecommunication systems is significantly subject to scattered signals superposed at the receivers. Notably, if the superposed scattered signals are impulsive in nature, this leads to burst effect on the communicated symbols. Thus, attaining an accurate estimate of the parameters of the underlying statistical model of the aggregate scattered signals becomes more important. More specifically, in the case of radar applications, finding efficient estimators for the amplitude distribution has been found to be of highly importance. In this article, the Bayesian approach has been adopted for parameter estimation of the amplitude distribution. To this end, within a Gibbs sampling framework, this would be done by the sampling from four full conditionals which can be performed in a straightforward manner. Depending on the size of sample, two types of priors, i.e. Jeffreys (small size) and conjugate (non-small size), are considered for the scale parameter of the amplitude distribution. While using the conjugate prior, hyperparameters can be found through the empirical Bayes. For the purpose of validation of the proposed approach, performance of the adopted Bayesian paradigm will be demonstrated through the simulation study. Furthermore, analysis through real datasets of radar signals reveals that the proposed Bayesian approach outperforms the known moment and log-moment estimators of the amplitude distribution and works better than the maximum likelihood estimator when sample size is small.

Signal Design and Multiple Target Detection in Presence of Clutter in Pulsed FDA Radar

Signal Design and Multiple Target Detection in Presence of Clutter in Pulsed FDA Radar

Observed Fisher information for radar clutter modeled with sub-Gaussian α-stable distribution

It is well known that the clutter in such applications as synthetic aperture radar (SAR) follows heavy-tailed distributions. In such applications, knowledge of the exact statistical properties and/or errors in estimation of the clutter parameters plays an important role in successful implementation of the related applications like target tracking using SAR. In this study, the observed Fisher information (OFI) matrix will be derived for the received clutter assuming that the statistical behavior of clutter follows a sub-Gaussian α-stable (SGS) distribution along with a density function for an SGS distribution based on a newly proposed expansion. The new expansion is characterized with high speed computation in one hand, and avoids issues related to numerical computation of SGS density function, on the other. The minimum standard error of the clutter parameters, then, will be derived as the OFI matrix is inversely related to the Cramer-Rao lower bound (CRLB) of the clutter's estimated parameters. In order to compute the density function and OFI of the SGS distribution, the performance of the proposed methodology has been tested through numerical simulation where the implemented computer code of the proposed numerical method has been demonstrated to be fast and accurate. The R code is available at https://github.com/mahditeimouri/Sub-Gaussian/blob/main/OFI-sub-Gaussian.r.

Simulation of radar sea clutter in correlated generalized compound distribution based on improved ZMNL

Simulation of radar sea clutter in correlated generalized compound distribution based on improved ZMNL

A Review on Parametric and Semiparametric Distributions in Characterizing Synthetic Aperture Radar Clutter Data

A Review on Parametric and Semiparametric Distributions in Characterizing Synthetic Aperture Radar Clutter Data

Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features

Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar’s simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method.