Sea Clutter Suppression Research Articles

Sea Clutter Suppression Based on Complex-Valued Neural Networks Optimized by PSD

Sea clutter suppression plays an important role in improving the estimation accuracy of the motion parameters of moving ships. Based on the chaotic characteristics of sea clutter, a novel sea clutter suppression method based on complex-valued neural networks optimized by power spectral density is proposed. The complex-valued neural networks helped reduce the phase prediction error of sea clutter, so that the sea clutter prediction accuracy was significantly improved compared with that of real-valued neural networks. The power spectral density function was added to the loss function of the complex-valued neural networks to optimize the training of the networks, and the prediction accuracy was further improved. The sea clutter could be effectively suppressed by cancellation and the signal-to-clutter ratio of the echo was improved. Experimental results based on measured sea clutter data validated the proposed method.

An Approach of Sea Clutter Suppression for SAR Images by Self-Supervised Complex-Valued Deep Learning

Strong reflections from the marine surface reduce the contrast between the target-of-interest and the background in synthetic aperture radar (SAR) images and severely affect the interpretation of the image. This letter proposes a framework of SAR sea clutter suppression based on a new self-supervised training strategy referred to as Clutter2Clutter (C2C), which mines self-supervised information from a large number of unlabeled SAR patches for network training. This letter also proposes a complex-valued UNet++ (CV-UNet++) network model to make full use of both amplitude and phase information of the complex SAR image, and the C2C strategy is used to train the CV-UNet++ for sea clutter suppression. Experiments on GF-3 and TerraSAR-X SAR data show that the proposed method has a better effect on suppressing sea clutter and is able to preserve the target-of-interest energy well.

A Low-Complexity MIMO Radar STAP Strategy for Efficient Sea Clutter Suppression

When sparse reconstruction (SR) space-time adaptive processing (STAP) strategy is employed for sea clutter suppression, the computational burden imposed by the spectrum reconstruction process is still a challenge. Hence, a low-complexity MIMO radar SR STAP strategy is developed via reducing model dimension and optimizing the sparse reconstruction scheme. On one hand, STAP echo model is projected into space and time domains owing to the uncoupled relationship, thus the computation burden involved with large measurement matrix can be lessened by low-dimension structure. Meanwhile, a coprime scheme is exerted to improve the degree of freedom (DOF) in sub models and ensure lower sampling consumption. On the other hand, in order to realize efficient spectral reconstruction, an improved SR algorithm is formulated via developing a prior matrix factor and a tradeoff factor. Convergence speed of the algorithm is promoted by tuning the factors reasonably, clutter spectrum is quickly accessible with less iterations so that adaptive filter can be constructed efficiently. According to the experiment results, high-efficiency spectral reconstruction is realized and effective clutter suppression performance can be ensured.

A Two-Stage Method for Ship Detection Using PolSAR Image

Ship detection using polarimetric SAR (PolSAR) images has recently been an active topic in the Earth observation field. There, how to detect small ships is an open and challenging issue. Within this context, we put forward a two-stage ship detection model, by which a novel ship detection method is proposed as well. Briefly, in the first stage, a suppression manipulation is adopted to suppress sea clutter, where the feature SVVS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>O</i></sub> is built on the intensity information with the orientation angle compensation (OAC). In the second stage, an enhancement manipulation is further executed to highlight ships from the suppressed sea clutter, where the features PID (polarimetric intensity difference) and NsD (nonsurface degree) are first constructed with SVVS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>O</i></sub> and a series of theoretical derivations. Then, via fusing PID and NsD together, the two-stage-based method FPAN is proposed to detect ships. To demonstrate its performance, we apply FPAN to four different L-Band PolSAR datasets. Experimental results reveal that, compared to other state-of-the-art methods, especially the DBSP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>CP</i></sub> method, FPAN is more effective in detecting small ships. On average, its figure-of-merit (FoM) and target-to-clutter ratio (TCR) values are, respectively 9.40% and 25.18% greater than those of DBSP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>CP</i></sub> , while the time consumption is just 58.67% of the latter.

Nonhomogeneous Sea Clutter Suppression Using Complex-Valued U-Net Model

This letter considers the problem of the target detection in the nonhomogeneous sea clutter environment, and proposes the complex-valued U-Net based clutter suppression method. Specifically, firstly, the complex signal features of radar echo sequences are extracted by developing the complex-valued convolutional blocks. Secondly, the complex multi-level features are fused, by employing the up-down sampling structure and skip connections, to suppress nonhomogeneous sea clutter. Further, the false alarm controllable detector is designed to detect the targets. Finally, the performance of the proposed method is evaluated via real data. The results show that it has a higher detection probability compared with the real-valued U-Net.

A Novel Sea Clutter Rejection Algorithm for Spaceborne Multichannel Radar Systems

Due to the high-speed movement of a spaceborne radar (SBR) platform, the geographic clutter spectrum expands severely, resulting in the useful moving target signal submerged by the main-lobe clutter background. To deal with this issue, the equipped multichannel arrays in an SBR system provide sufficient spatial degrees, and as a consequence, the space-time adaptive processing (STAP) technology is often preferred to achieve the moving target detection, even in the main-lobe clutter regions. However, for the moving target detection under the sea scene, due to the complex internal motion of sea clutter, the clutter signal received by an SBR system may possess the space- and time-varying characteristics, worsening the multichannel clutter rejection performance using the traditional STAP techniques. In this article, a novel sea clutter suppression method based on the joint space-time-frequency adaptive filtering is proposed. In the proposed algorithm, according to the coherent time analysis of sea clutter, the subaperture time-domain sliding window is employed to alleviate the clutter decorrelation effect, and then, a modified subspace projection technique is applied to accomplish the first-stage clutter rejection. After realizing the effective signal recovery with respect to these residual subaperture clutter data, the second-stage spatial filtering method is applied to realize the final clutter suppression with respect to the relatively high Doppler resolution clutter returns. The effectiveness of the proposed algorithm is verified by both simulated multichannel sea clutter data and real-measured sea clutter data.

Sea clutter suppression algorithm in spatial domain based on singular value decomposition

In order to detect the targets in the first-order sea clutter spectrum of high frequency surface wave radar (HFSWR), a method based on singular value decomposition (SVD) is proposed to suppress sea clutter in spatial domain. The sea clutter suppression method is based on the strong correlation of sea clutter, thus we take the adjacent range bin as the reference. Then the sea clutter subspace and noise subspace in the spatial domain is estimated by singular value decomposition of its array covariance matrix. Finally, we subtract its projection in the sea clutter subspace from the array signal with the orthogonality of subspace, and achieves the purpose of suppressing sea clutter in the spatial domain. Compared with the existing methods of sea clutter suppression in spatial domain, there is no need to know the azimuth of sea clutter in advance. The subspace is estimated by singular value decomposition of its array covariance matrix, which makes it easier and more accurate to estimate the sea clutter subspace and noise subspace. The result shows that the method improves the signal to clutter plus noise ratio (SCNR), which is benefit to target detection.

Sea Clutter Covariance Matrix Estimation and Its Application to Whitening Filter

The accurate estimation of clutter covariance matrix (CCM) is essential in designing a radar detector/filter to suppress sea clutter. This estimation might not be easily accomplished because of the scarcity of valid training vectors adjacent to the range cell under test (CUT). We propose a new CCM estimation algorithm that is derived by modeling time-series clutter returns into a clutter Doppler spectrum in the frequency domain and exploiting mutual independence among spectral components. To justify its excellence over the conventional sample covariance matrix (SCM) algorithm, we design two filters—a maximum signal-to-interference-plus-noise ratio (SINR)-based filter and a whitening filter—that use the estimated CCMs and compare their performance in a numerically simulated sea clutter scenario. Comparisons are made by showing the eigenvector spectra of the estimated CCMs and the frequency responses and outputs of the filters. Moreover, SINRs at the target Doppler bin are examined and compared with a theoretical, analytically derived SINR.

Low observable group targets detection based on space–frequency cascaded adaptive processing for MIMO OTH radar

The spread of sea clutter in the Doppler domain caused by multi-mode propagation would make it difficult for over-the-horizon radar to detect low observable group targets. To solve this problem, a multi-mode clutter suppression method based on space–frequency cascaded adaptive processing is proposed by utilizing the spatial diversity characteristics of multiple-input–multiple-output radar. Then, the low observable group targets can be detected. The proposed method adopts the idea of space–frequency stepwise processing. First, the multi-stage Wiener filter ESPRIT algorithm is exploited to estimate the direction of departure and direction of arrival of different path signals, and then the multi-mode clutter is adaptively transformed into single-mode clutter based on the estimated angles. Second, the atomic norm minimization algorithm and Vandermonde decomposition are used to estimate the steering vector of the first order Bragg frequency of sea clutter, whose path contains the group targets. Finally, the least squares algorithm is used to realize the adaptive suppression of sea clutter in the frequency domain. Because there is no need to estimate the signal covariance matrix and its eigenvalue decomposition and conduct spectral peak searching, the proposed method greatly reduces the computation of the angle estimation. Compared with the existing second-order blind identification and spatial smoothing SOBI algorithms, the proposed algorithm does not need the information of the first order Bragg frequency of sea clutter, so it can improve the output signal-to-clutter-noise ratio when there is an error in the first order Bragg frequency of sea clutter. Therefore, the proposed algorithm is more conducive to the detection of low observable group targets. Simulation results are given to demonstrate the effectiveness of the proposed method.

First-Order Sea Clutter Suppression for High-Frequency Surface Wave Radar Using Orthogonal Projection in Spatial–Temporal Domain

The broadening first-order sea clutters caused by the signals from different directions with various radial current velocities create severe disturbance for target detection using high-frequency surface wave radar (HFSWR). Conventional sea clutter suppression methods tend to remove the sea clutter and target signals when they are mixed in the Doppler spectrum. Based on the characteristics of the target signal and sea clutter in spatial–temporal domain, a new first-order sea clutter suppression method for HFSWR using orthogonal projection is proposed. The proposed method uses the data from multichannels and slow-time domain at the adjacent range cell to construct a covariance matrix, which can be used to obtain the sea clutter subspace by eigendecomposition. Later, original signals are projected onto the sea clutter subspace. Finally, subtract the component of the original signals in the sea clutter subspace from the original signals to achieve the suppression of sea clutter by retaining the target signals. The simulation and experimental results for a single target and multiple targets cases indicate that the proposed method can suppress the first-order sea clutter effectively, which enhances the target detection capacity in the sea clutter zone for HFSWR.

Detection of Target and Suppression of Sea and Weather Clutter in Stormy Weather by Weibull/CFAR

AbstractIn stormy weather due to typhoon approaching the Izu Islands, I have observed sea and weather clutter containing a target (ship) using an S‐band radar with a frequency of 3.05 GHz, a beamwidth of 1.8°, a pulsewidth of 0.5 μs, and a pulse‐repetition frequency of 1600 Hz. To analyze such clutter amplitude statistics, I have used partial Q–Q plot, which is more rigorous fit of the distribution to the data than conventional Q–Q plot. It has been found that the sea and weather clutter amplitudes obey the Weibull distribution with the shape parameters of c = 6.23 for entire data and c = 5.83 to 8.78 for small data within the beamwidth of an antenna. I have applied Weibull/CFAR (constant false alarm rate) circuit configured by modifying cell‐averaging (CA) LOG/CFAR circuit to the observed data. It has been found that ship is detected, and sea and weather clutter is suppressed with improvement of 33.61 dB by Weibull/CFAR. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Effective Sea Clutter Suppression via MIMO Radar Space–Time Adaptive Processing Strategy

Due to nonlinear varying motions of the physical sea surface, sea clutter suppression is challenging with the broadened and rapidly changed Doppler spectrum content. In this article, an effective sea clutter suppression scheme based on multiple-input–multiple-output (MIMO) radar is presented via studying sea clutter features and reconstructing clutter spatial-Doppler spectrum accurately. To depict complicated Doppler features more precisely, the multistage Gaussian mixture model (GMM) is first developed in this article. In terms of exploring the dominant Doppler characteristics, GMM is more applicable in dynamic sea environments, including main swell and breaking waves. Afterward, for accurate reconstruction of clutter spectrum, the MIMO radar Gaussian mixture prior space–time adaptive processing (GMP-STAP) scheme is proposed for accurate estimation of clutter features. The Kullback–Leibler (KL) divergence is developed, and approximate distribution of posterior probability density functions (PDF) can be explored with low information loss based on complicated prior structure. Closed-form solutions of variables and parameters can be formulated through the minimization of KL divergence. The robust multistage Doppler power spectrum of sea clutter can be reconstructed accordingly. Moreover, considering that the independent and identically distributed (IID) property is easily broken among reference samples, the effective cross-correlation analysis is launched to keep statistical characteristics identical and reduce statistical errors. Finally, according to the experiments, accurate reconstruction of sea clutter features can be realized, and effective sea clutter suppression performance can be illustrated.

Weak Target Detection in the Presence of Sea Clutter Using Radon-Fractional Fourier Transform Canceller

In this article, we propose a new weak maneuvering target detection method in a sea environment with a Radon-fractional Fourier transform (RFrFT) canceller, which can efficiently suppress the sea clutter and detect targets with low computations. To accomplish sea clutter suppression, we design a canceller based on the RFrFT in the detection process, which utilizes different representations of the maneuvering target and the sea clutter. A nonuniform searching strategy with a nonuniform searching interval of fractional order is presented to reduce the computation of the detection process. Moreover, to make the RFrFT canceller also effective for the multiple-target case, we propose a bandstop design on the basis of the RFrFT convolution theorem by making use of the detection results after using the canceller. After that, the detailed detection algorithm is described. Experimental results on simulated and real data show that the proposed method is superior compared with other methods.

Sea Clutter Suppression Method of HFSWR Based on RBF Neural Network Model Optimized by Improved GWO Algorithm.

The detection performance of high-frequency surface-wave radar (HFSWR) is closely related to the suppression effect of sea clutter. To effectively suppress sea clutter, a sea clutter suppression method based on radial basis function neural network (RBFNN) optimized by improved gray wolf optimization (IGWO) algorithm is proposed. Firstly, according to shortcomings of the standard gray wolf optimization (GWO) algorithm, such as slow convergence speed and easily getting into local optimum, an adaptive division of labor search strategy is proposed, which makes the population have abilities of both large-scale search and local exploration in the entire optimization process. Then, the IGWO algorithm is used to optimize RBFNN, finally, establishing a sea clutter prediction model (IGWO-RBFNN) and realizing the prediction and suppression of sea clutter. Experiments show that the IGWO algorithm has significantly improved convergence speed and optimization accuracy. Compared with the particle swarm algorithm with linear decreasing weight strategy (LDWPSO) and the GWO algorithm, the RBFNN prediction model optimized by the IGWO algorithm has higher prediction accuracy and has a better suppression effect on sea clutter of HFSWR.

A convolutional neural network based approach to sea clutter suppression for small boat detection

Current methods for radar target detection usually work on the basis of high signal-to-clutter ratios. In this paper we propose a novel convolutional neural network based dual-activated clutter suppression algorithm, to solve the problem caused by low signal-to-clutter ratios in actual situations on the sea surface. Dual activation has two steps. First, we multiply the activated weights of the last dense layer with the activated feature maps from the upsample layer. Through this, we can obtain the class activation maps (CAMs), which correspond to the positive region of the sea clutter. Second, we obtain the suppression coefficients by mapping the CAM inversely to the sea clutter spectrum. Then, we obtain the activated range-Doppler maps by multiplying the coefficients with the raw range-Doppler maps. In addition, we propose a sampling-based data augmentation method and an effective multiclass coding method to improve the prediction accuracy. Measurement on real datasets verified the effectiveness of the proposed method.

Sea Clutter Suppression for Radar PPI Images Based on SCS-GAN

The problem of strong sea clutter, e.g., sea spikes, may bring in low signal-to-clutter ratio (SCR) and cause great interference to radar marine target detection. However, the sea clutter suppression ability of current algorithms is limited with poor generalization under complex marine environment. In this letter, a novel sea clutter suppression generative adversarial network (SCS-GAN) is designed and employed for marine radar plan-position indicator (PPI) images detection. The SCS-GAN is based on residual networks and attention module, which includes residual attention generator (RAG) and sea clutter discriminator (SCD). In order to expand the data sets and improve generalization ability, clutter-free data set A, simulated sea clutter data set B (containing five types of sea clutter distributions), and actual sea clutter data set C are constructed by means of simulation and acquisition of real radar returns. At last, the parameter, i.e., clutter suppression ratio (CSR) is designed for evaluating the sea clutter suppression performances of the proposed method and other denoising and clutter suppression methods including CBM3D, denoising convolutional neural network (DnCNN), FFDNet, and Pix2pix. After testing with actual data, it is proved that the SCS-GAN has faster clutter removal speed, stronger generalization ability, and at the same time marine targets in images are remained completely.

Blind Sea Clutter Suppression for Spaceborne GNSS-R Target Detection

It has been demonstrated that delay-Doppler (DD) maps (DDMs) retrieved from spaceborne Global Navigation Satellite Systems Reflectometry missions contain sea clutter and also potential sea targets. Examples of sea targets include sea ice, oil rigs, and oil slicks. To distinguish sea targets from sea clutter, one can estimate the sea clutter and subtract it from the DDM, leaving behind only the sea target's signature on the DDM. This is computationally expensive and will not be robust when the sea clutter estimate is poor or if there are DD tracking errors. This article presents two blind methods for the suppression of sea clutter: first, an adaptive filter and second, an infinite impulse response low-pass filter. We use real DDMs collected by TechDemoSat-1 to show the autoregressive, hence temporally correlated nature of the DDM time series, and present evidence that two blind methods can suppress clutter at least as effectively as the existing method, albeit at a fraction of its computational cost.

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.

Study on Sea Clutter Suppression Methods Based on a Realistic Radar Dataset

To improve the ability of radar to detect targets such as ships with a background of strong sea clutter, different sea-clutter suppression algorithms are developed based on the realistic Intelligent PIxel processing X-band (IPIX) radar datasets, and quantitative research is carried out. Four algorithms, namely root cycle cancellation, singular-value decomposition (SVD) suppression, wavelet weighted reconstruction, and empirical mode decomposition (EMD) weighted reconstruction and their corresponding suppression methods are introduced. Then, the differences between the four algorithms before and after sea-clutter suppression are compared and analyzed. The average clutter-suppression and target-suppression amplitudes are selected as measures to verify the suppression effect. Sea-clutter data collected in the high-sea state, low-sea state, near-sea area, and far-sea area are selected for statistical analysis after suppression. All four methods have certain suppression effects, among which EMD reconstruction is best, reaching an average clutter-suppression range of 15.507 dB and a signal-suppression range of about 1 dB, which can improve the ability of radar to detect targets such as ships with a background of strong sea clutter.

Low-Velocity Small Target Detection With Doppler-Guided Retrospective Filter in High-Resolution Radar at Fast Scan Mode

It is a difficult task for high-resolution maritime radars operating at a fast scan mode to find sea-surface floating and low-velocity small targets due to ubiquitous sea clutter, sporadic sea spikes like target returns, and shortage of shared database. In this paper, a simulation method is presented to generate high-resolution radar returns of a local sea surface with a structural trend in texture and sea spikes by integrating the existing results on large-scale sea surface generation, sea surface reflectivity, Doppler characteristics of sea clutter, and properties of sea spikes. Generally, sea-surface small target detection at a fast scan mode is composed of intrascan integration to suppress sea clutter and interscan integration to exclude false alarms and sea spikes. Based on the Doppler difference between targets and sea clutter at the two time scales of a coherent processing interval (CPI) of tens of milliseconds and a scan period of several seconds, a Doppler-guided retrospective filter (DGRF) detector is proposed, which uses the optimum coherent detection in intrascan integration and a DGRF in interscan binary and test statistic integrations. The two integrations and Doppler consistency of integrated plots are fused for the final decision. Owing to the Doppler guidance, the proposed detector effectively impedes the integration of false alarms of the intrascan processing and provides significant detection performance improvement, which is verified by the simulated data and real radar data with test small targets.