Sea Clutter Research Articles

A novel sea clutter suppression method based on SVD‐FRFT at low signal‐to‐clutter ratio

AbstractThe target and sea clutter Doppler domains frequently overlap due to the frequent passage of slow ship targets through the sea clutter zone. In this letter, a novel sea clutter suppression method is suggested as a solution to this issue, whose key is a novel singular value zeroing criterion guided by the search results of two‐dimensional spectral peaks. Verified by simulations, the method proposed can improve signal‐to‐clutter ratio (SCR) from −8 to 41 dB in the frequency domain and be more effective than the conventional SVD‐FRFT (a method for joint application of singular value decomposition and fractional Fourier transformer) and improved SVD‐FRFT.

A sea clutter suppression algorithm for over-the-horizon radar based on dictionary learning and subspace estimation

Detecting a ship target by over-the-horizon radar (OTHR) is seriously affected by sea clutter. This paper proposes a clutter suppression algorithm based on dictionary learning and subspace estimation (DLSE). First, the K-singular value decomposition (K-SVD) is used for dictionary learning and sparse representation of the sea clutter. Then, the orthogonal matching pursuit (OMP) is used to reconstruct the sea clutter component to suppress the outliers. Finally, the clutter covariance matrix is calculated using the reconstructed clutter spectrum, and the clutter subspace of the covariance matrix is estimated to achieve clutter suppression. In this paper, the K-SVD is improved to preserve the phase information of the complex signal. The sea clutter spectrum, after sparse representation and reconstruction, is not impacted by outliers in neighboring cells. Therefore, the clutter covariance matrix has a better estimate accuracy than existing subspace decomposition algorithm. The adaptive parameter estimate method also enhances the adaptability of the DLSE. The experimental results of real data demonstrate that the DLSE is as sophisticated as existing subspace decomposition algorithms while having a higher output signal-to-clutter-plus-noise ratio (SCNR) and peak-to-side lobe ratio (PSLR), improving about 2-5 dB. Results of outlier suppression attest to the robustness of the DLSE. Under various coherent integration times (CIT) and frequencies, the DLSE has the best clutter suppression performance among the comparison algorithms.

Sea Clutter Amplitude Prediction via an Attention-Enhanced Seq2Seq Network

Sea clutter is a kind of ubiquitous interference in sea-detecting radars, which will definitely influence target detection. An accurate sea clutter prediction method is supposed to be beneficial while existing prediction methods are based on the one-step-ahead prediction. In this paper, a sea clutter prediction network (SCPNet) is proposed to achieve the k-step-ahead prediction based on the characteristics of sea clutter. The SCPNet takes a sequence-to-sequence (Seq2Seq) structure as the backbone, and a simple self-attention module is employed to enhance the ability of adaptive feature selections. The SCPNet takes the normalized amplitudes of sea clutter as inputs and is capable of predicting an output sequence with a length of k; the phase space reconstruction theory is also used to find the optimized input length of the sea clutter sequence. Results with the sea-detecting radar data-sharing program (SDRDSP) database show the mean square error of the proposed method is 1.48 × 10−5 and 8.76 × 10−3 in the one-step-ahead prediction and the eight-step-ahead prediction, respectively. Compared with four existing methods, the proposed method achieves the best prediction performance.

Ship Detection in PolSAR Images Based on a Modified Polarimetric Notch Filter

Ship detection based on synthetic aperture radar (SAR) imagery is one of the key applications for maritime security. Compared with single-channel SAR images, polarimetric SAR (PolSAR) data contains the fully-polarized information, which better facilitates better discriminating between targets, sea clutter, and interference. Therefore, many ship detection methods based on the polarimetric scattering mechanism have been studied. To deal with the false alarms caused by the existence of ghost targets, resulting from azimuth ambiguities and interference from side lobes, a modified polarimetric notch filter (PNF) is proposed for PolSAR ship detection. In the proposed method, the third eigenvalue obtained by the eigenvalue–eigenvector decomposition of the polarimetric covariance matrix is utilized to construct a new feature vector. Then, the target power can be computed to construct the modified PNF detector. On the one hand, the detection rate of ship targets can be enhanced by target-to-clutter contrast. On the other hand, false alarms resulting from azimuth ambiguities and side lobes can be reduced to an extent. Experimental results based on three C-band AIRSAR PolSAR datasets demonstrated the capability of the proposed PNF detector to improve detection performance while reducing false alarms. To be specific, the figure of merit (FoM) of the proposed method is the highest among comparative approaches with results of 80%, 100%, and 100% for the tested datasets, respectively.

Detection and recognition of dim and small targets in sea clutter background based on polarization decomposition

The consideration of the polarization scattering characteristics of radar echo in the detection of small and dim targets in a sea clutter background leads to high detection probability and high recognition accuracy. In this study, a target detection method based on canonical polar decomposition (CPD) was developed. In particular, a polarization matrix T was constructed for the radar echo of each range bin; the absolute value A and phase [Formula: see text] of T were extracted; the diagonal elements of the characteristic matrix H were extracted and converted into the singular spectrum form; and the negative entropies [Formula: see text] and [Formula: see text] of the singular spectrum were calculated. We set [Formula: see text]; then, the negative entropy value [Formula: see text] of each range bin was obtained, and the range bin of the minimum value was identified, which was the target position. Thereafter, a seven-component scattering power decomposition (7SD) was performed on T. The basic scattering structure of the target was analyzed, and a polar feature description word (PFDW) was formed to recognize the target characteristics. The effectiveness of the proposed method was verified by using the sea clutter dataset obtained with the IPIX radar. The comparison with the statistical model-based method is also made for indicating the advantage of the proposed method.

Antenna Beampattern Matched Optimum Coherent Detection in High-Resolution Mechanically Scanning Maritime Surveillance Radars

Mechanically scanning radars are still widely used in ocean guard and surveillance areas nowadays, owing to simple structure, light weight and low cost. The antenna rotation causes amplitude modulation of target returns along pulses by the antenna azimuth beampattern. The traditional coherent detection that ignores this modulation suffers from some performance loss. In this paper, an antenna beampattern matched optimum coherent detector and a dual-threshold detection scheme are proposed for high-resolution mechanically scanning maritime surveillance radars. Firstly, it is shown that the modulation of target returns is determined by the antenna azimuth beampattern, scan rate, and pulse repetition interval (PRI) and the antenna scanning also affect the temporal correlation of sea clutter. Secondly, the antenna beampattern matched optimum coherent detector is given in compound-Gaussian sea clutter with inverse Gamma distributed texture and the pulse scanning mode is used to deal with unknown target azimuth angle in the matched filter. Thirdly, to mitigate the high computational cost of the pulse scanning mode, a dual-threshold scheme is given, which searches all pulse-range cells by short-time integration and further confirms suspected cells whose test statistics are between the two lower and higher thresholds in the search by long-time integration. Moreover, two fast recursive clutter covariance matrix inversion algorithms along pulses and range cells are presented, which bring a great reduction in computational complexity. Finally, real measured radar data is used to verify the proposed detector and scheme and the experimental results show that it behaves better in detection of small targets than the traditional detectors.

Track-Before-Detect Using an Airborne Multichannel Radar in the Maritime Domain

This paper explores methods for detection and tracking of maritime targets using an airborne scanning multi-channel radar, operating at high altitudes. The main technical difficulties include: (a) the non-stationary and non-Gaussian nature of sea clutter when using a high-resolution airborne radar, and (b) low target signal-to-interference ratios due to higher grazing angles. It is proposed to carry out processing in two stages. First, space time adaptive processing (STAP) is applied to exploit the spatial and temporal degrees of freedom available in the multi-channel radar. Subsequently, target detection and tracking is performed using the track-before-detect method, applied to the STAP output. For the sake of comparison, a more traditional tracking method is also developed, where a detection threshold is applied to the STAP output, to obtain point measurements, subsequently processed via an extended target tracking methodology. The error performance of the proposed methodology is presented, using high-fidelity radar data generation.

Shape Parameter Estimation of K-Distributed Sea Clutter Using Neural Network and Multisample Percentile in Radar Industry

In this paper, we consider the problem of robustly and accurately estimating the shape parameters of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -distributed sea clutter in the maritime radar industry. Outliers formed by non-sea-surface echoes have a significant negative impact on the estimation accuracy of the shape parameters. To improve the estimation accuracy, we first propose a bipercentiles feed forward network for the shape parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> (BP-FFNN- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> ), which makes use of a ratio of two percentiles and a two-hidden-layer feed forward neural network. The BP-FFNN- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> can learn the mathematical relationship between the shape parameter and the ratio of two percentiles, and can work in environments where the number of outliers is approximately known. In addition, to deal with the case where the number of outliers is not known due to dynamic changes in the environment, we also design another neural network (referred to as MBP-FFNN- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> ) which consists of multiple BP-FFNNs- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> and a multi-class classification network. The MBP-FFNN- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> can perceive the change in the proportion of outliers, so an accurate estimate of the shape parameter can be obtained from a BP-FFNN- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> that is not affected by outliers. Finally, training and test data are constructed to train and evaluate the two proposed estimation methods, respectively. Experimental results demonstrate that the proposed BP-FFNN- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> performs better than traditional moments-based estimators, and has almost the same performance as the tri-percentile estimator. Compared with the tri-percentile estimator, the BP-FFNN- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> avoids frequent table lookups, and produces a continuous estimate. The proposed MBP-FFNN- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> can achieve more than 97% overall classification accuracy on simulated and measured data, and thus an accurate estimate of the shape parameter can be obtained when the number of outliers varies.

Ocean Clutter Characterization Based on PolSAR Data and Second-Order Statistics of Elementary Scatterers

New features are proposed for sea clutter characterization when PolSAR data are employed. Cameron coherent target decomposition is applied to characterize each SAR pixel by means of the 8 basic elementary scatterers obtained by this decomposition. Since the examined SAR pixel does not match exactly to a specific ideal elementary scatterer, the closest scatterer is assigned to this pixel along with its distance (closeness) from the ideal one. The features proposed are (a) the percentage of each elementary scatterer in the investigated region; (b) the distribution of the closeness of each declared elementary scattering mechanism to its ideal counterpart; (c) the co-occurrence of the same scattering mechanism, taking into consideration its distance from the ideal one; and (d) The co-occurrence of the different scattering mechanisms in pairs, taking again into consideration their distance from the ideal ones. Simultaneously, the skewness and the kurtosis and their 2D versions of the previously mentioned probabilities are employed to further improve ocean clutter characterization. The above features correspond to the sea state condition in each separate region, i.e., the wave height and the wind speed. A clear correspondence between the proposed features and the sea state conditions is established. Data were available from RADARSAT-2 and ALOS-1 PALSAR systems for four different ocean regions on Earth.

Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization

In order to reduce the sea clutter interference in the detection of sea surface targets, we propose a bistatic sea clutter suppression method based on compressed sensing optimization in this paper. The proposed method mitigates the interference effect by reconstructing and cancelling out the sea clutter. Since the fixed sparse base is not always completely applicable for the sparse representation of sea clutter, we propose a sparse base optimization algorithm based on transfer learning to convert the fixed sparse base into an adaptive one. Moreover, we introduce a sensing matrix optimization algorithm to decrease the cross-correlation coefficient between the measurement matrix and the sparse base matrix, which can enhance the signal reconstruction quality. Finally, we use the orthogonal matching pursuit algorithm to reconstruct the sea clutter and employ the reconstructed results to cancel and suppress the sea clutter. The simulation results demonstrate that the proposed method outperforms the traditional methods such as the root time-domain cancellation method and the singular value decomposition method (SVD). Therefore, the proposed method has great practical significance for the detection of bistatic sea surface targets.

Open-Source Data Processing Chain for Marche Region X-band Weather Radar

In the framework of three projects co-funded by the European Union (EU), an X-band (8-12 GHz frequency range) polarimetric radar was installed in the Marche Region territory (East-Central Italy) at Cingoli municipality, province of Macerata. The radar site is located at about 750 m above sea level and about 30 km away from the Adriatic Sea. The radar, managed by the Marche Region Civil Protection Service, is employed for weather monitoring purposes and is in pre-operational stage. It is known that radar measurements are affected by various sources of error, to be addressed in order to improve the accuracy of final products. Among these, the most important are radar calibration, ground and sea clutter, beam blockage, rain attenuation, wet-radome attenuation, beam-broadening, non-uniform beam filling, vertical variability of precipitation and wireless local-area-network (WLAN) interferences. Nowadays quantitative rainfall estimation using X-band weather radar are essential to meet requirements for flood forecasting, water management and many hydro-meteorological applications. Besides higher resolution, X-band radars are cost-effective compared to S- or C-band radars because of smaller antenna size. On the other hand, main disadvantages of such systems are the large influence of attenuation by liquid water and a relatively short range. In this work, we will present the data-processing chain developed ad hoc in order to remove or at least reduce the sources of error affecting Cingoli radar data. The performance of the data-processing chain was evaluated in the light of case studies related to meteorological events that interested Marche region territory in the last two years. The software was developed using open source technology. The current version of the chain does not take into account the echoes from sea clutter and the attenuation due to wet radome that can be significant at X-band; such issues will be addressed in a future work.

Marine radar monitoring IoT system and case study of target detection based on PPI images

AbstractTo ensure the ship navigation safety, it is necessary to detect the targets in the background of sea clutter around the ship. The most commonly used target detection equipments for ship navigation are marine radar and automatic identification system (AIS) device. However, marine radar echoes are often mixed with multiple clutter, and weak targets are not always equipped with AIS device, neither provides AIS information with low accuracy, leading to difficulty with target detection. Moreover, existing marine monitoring systems are accustomed to using the traditional information fusion methods for target detection, and the accuracy of identifying the weak targets is relatively low. To make full use of radar echo and AIS information and improve the accuracy of target detection, a Marine radar monitoring Internet of Things system is proposed, in which marine radars work in both scanning and staring modes. By adopting image segmentation and deep learning methods, the proposed design can enable accurate perception of weak target detection based on plan‐position indicator (PPI) images. A case study based on the selected X‐band radar datasets shows that the proposed design can achieve high target identification accuracy.

Sea Clutter Measurement Test and Amplitude Characteristics Analysis in the South China Sea Nearshore Area

To address the problem of sea clutter amplitude distribution characteristics varying with bandwidth, this paper obtains the raw data of clutter under different polarization methods of HH and VV in X and Ka bands based on the sea clutter measurement experiment carried out in June 2022 in the near-shore waters of South China Sea. For the data in X-band HH polarization mode, the goodness-of-fit test was conducted based on the classical models of Rayleigh, Weibull, log-normal and K distribution for the amplitude distribution characteristics of the sea clutter, and the variation of the estimated parameters of the distribution model with the bandwidth was analyzed. The test data show that: 1, with the increase of bandwidth sea clutter amplitude decreases; 2, with the increase of bandwidth Weibull distribution model parameters are decreasing trend, k distribution parameters are increasing trend; 3, the test conditions, the offshore sea clutter amplitude distribution is the optimal K distribution. The conclusions of this paper can provide a comparison and reference for the subsequent study on the characteristics of sea clutter distribution.

Enhanced CNN-Based Small Target Detection in Sea Clutter With Controllable False Alarm

As targets floating on the sea surface get more invisible, it is becoming vital and challenging to effectively detect small targets from strong sea clutter. Besides, fitting the distribution of sea clutter is a hard task because of the complex characteristics of clutter. Classic model-based detectors are prone to suffer from the mismatch problem and a high probability of false alarm (PFA). In this paper, a PFA controllable and data-driven detection method based on an attention-enhanced convolutional neural network (CNN) is proposed. Different from mainstream CNN-based detectors, the proposed method takes time-frequency maps obtained by the Wigner-Ville distribution (WVD) as inputs. Then time-frequency maps are converted into feature images as inputs to the designed CNN strengthened with representation powers and feature refinement abilities. The designed CNN is capable of automatically learning and classifying different features between targets and clutter. Meanwhile, a PFA control unit is employed to ensure an expected actual PFA. Results with the IPIX database show that the probability of detection of the proposed method is about 0.9066 with the PFA being 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and the observation time being 1.024 s. Compared with five typical feature-based detectors, the proposed method achieves better detection performance. Besides, results with the Sea-detecting Radar Data-sharing Program database also verify the feasibility and superiority of the proposed detector. The source codes are available at https://github.com/quqizhe-whu/ADN.

Maritime Infrared Target Detection Using a Dual-Mode Background Model

With the rapid development of marine business, the intelligent detection of ship targets has become the key to marine safety. However, it is difficult to accurately detect maritime infrared targets due to severe sea clutter interference in strong wind waves or dim sea scenes. To adapt to diverse marine environments, a dual-mode sea background model is proposed for target detection. According to the global contrast of the image, the scene is divided into the sea surface with violent changes and the sea surface with stable changes. In the first stage, the preliminary background model suitable for steadily changing scenes is proposed. The pixel-level foreground mask is generated through the background block filter and the posterior probability criterion. Moreover, the learning rate parameter is adjusted using the detection results of two adjacent frames. In the second stage, the background model suitable for highly fluctuating scenes is proposed. Moreover, the local correlation feature is used to enhance the local contrast of the frame. The experimental results for the different scenes show that the proposed method has a better detection performance than the other comparison algorithms.

Sea Surface Moving Target Detection Using a Modified Keystone Transform on Wideband Radar Data

The echoes collected by wideband radar systems provide abundant information on target scatterers, which is beneficial to target detection, classification, and recognition. However, as the radar range resolution increases, range cell migration (RCM) during the coherent integration (CI) period happens much easier, which may cause a degradation of target detection probability. In addition, due to the target’s orientation and structure relative to the radar, the distribution characteristics of the target scatterers in high-resolution range profiles (HRRPs) and the detection window length may vary from pulse to pulse, which may reduce the performance of traditional energy integration (EI) detectors. To solve those problems, moving range-spread target (RST) detection combining the modified keystone transform (MKT) and improved EI (IEI) is proposed in this paper. Firstly, based on waveform entropy minimization, MKT using hunter–prey optimization (HPO) is introduced to reduce the CI gain loss. The target Doppler ambiguity factor is estimated using such an effective optimization technique. Then, the IEI detector optimized by the adaptive threshold and detection window is utilized to achieve target detection, which minimizes the sensitivity of the traditional EI detector to the detection window length. The proposed method significantly improves the performance of moving RSTs in sea clutter without prior knowledge of the target Doppler ambiguity factor. Experiments are conducted by comparing the proposed method with other competing methods on both simulation data and real sea clutter data. The results demonstrate that the proposed method can obtain the CI more efficiently and has a higher detection probability.

A Weak Target Detection Algorithm IAR-STFT Based on Correlated K-distribution Sea Clutter Model

A Weak Target Detection Algorithm IAR-STFT Based on Correlated K-distribution Sea Clutter Model

A Clutter Parameter Estimation Method Based on Origin Moment Derivation

Parameter estimation is significant to prediction and estimation in the field of radar clutter characteristics. Therefore, it is necessary to study the problem of parameter estimation. The K-distribution is a commonly used model in sea clutter, which is a two-parameter model with shape parameters and scale parameters. The value of the shape parameters should be greater than 0. Moment estimation is usually used to estimate the parameters of the K-distribution. It overcomes the disadvantage of large computation compared with the maximum likelihood estimation method. However, the moment estimation usually uses two different order origin moments to solve the parameters. The joint solution of different order will cause large calculation errors, and sometimes the shape parameter is estimated to be less than 0. In the origin moment expression, the order k can be regarded as a continuous variable. By calculating the relationship between the k-order origin moment and its derivative, a parameter estimation method based on the origin moment derivative is proposed. The estimation efficiency and accuracy are compared with some moment estimation methods. Both simulation data and measured clutter data show that this method can achieve 100% estimation efficiency, can obtain higher estimation accuracy, and can also avoid the situation where the estimated value of the shape parameter is less than 0. Using the same idea to estimate the parameters in the two-parameter models, log–normal and Weibull distribution, we can also obtain the parameters with higher estimation accuracy. The experiments show that the higher-order origin moments are sensitive to the data, and the lower-order moments should be selected as far as possible. By selecting the appropriate order k, we can obtain ideal estimation parameters.

Ship Detection in Nonhomogeneous Sea Clutter Based on Polarization-Time–Frequency Optimal Using Polarimetric SAR

Synthetic aperture radar (SAR) ship target detection under nonhomogeneous sea conditions is changeable. In this article, according to the characteristics of the target and ocean during SAR imaging, the polarization-time–frequency coherent optimal detector PTFO is constructed, and then the constant false alarm rate method is used to detect ship targets with a stable scattering in SAR images. Four quad-polarimetric RADARSAT-2 data are used to analyze the ship–clutter contrast enhancement capability of PTFO quantitatively, and the appropriate number of time–frequency decompositions is determined to be 3. The proposed method can obtain an FOM of 0.95, which is better than other classical methods to control the detection accuracy and suppress the appearance of false alarm targets.

Infrared Small Marine Target Detection Based on Spatiotemporal Dynamics Analysis

It is a big challenge to detect and track small infrared marine targets in non-stationary and time-varying sea clutter because the signal is too strong to be estimated. Based on the phenomenon that sea clutter spreads not only in the temporal domain but also in the spatial domain, this paper proposes an infrared small marine target detection algorithm based on spatiotemporal dynamics analysis to improve the performances of sea clutter suppression and target detection. The moving sea clutter is modeled as the spatial-temporal phase space, and the dynamical parameters of the sea clutter in the spatiotemporal domain are extracted from the sea clutter image sequence. Afterwards, the temporal dynamics reconstruction function and the spatial dynamics reconstruction function are built based on these extracted dynamical parameters. Furthermore, the space-time coupling coefficient and the spatiotemporal dynamics reconstruction function are estimated by means of a radial basis function (RBF) neural network to reconstruct the propagation regularity of the moving sea clutter. Finally, the sea clutter is suppressed by subtracting the estimated image from the original image, and then the target is detected in the suppressed image using the constant false alarm rate (CFAR) criteria. Some experiments on the small marine target in various fluctuating sea clutter image sequences are induced, and the experimental results show that the proposed algorithm could achieve outstanding performances in sea clutter suppression and small target detection.