Radar Sea Clutter Research Articles

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.

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.

Statistical Parameters Extracted from Radar Sea Clutter Simulated under Different Operational Conditions.

A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on.

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.

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

Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Images

Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Images

Multi-headed deep learning-based estimator for correlated-SIRV Pareto type II distributed clutter

This paper deals with the problem of estimating the parameters of heavy-tailed sea clutter in high-resolution radar, when the clutter is modeled by the correlated Pareto type II distribution. Existing estimators based on the maximum likelihood (ML) approach, integer-order moments (IOM) approach, fractional-order moments (FOM), and log-moments (log-MoM) have shown to be sensitive to changes in data correlation. In this work, we resort to a deep learning (DL) approach based on a multi-headed architecture to overcome this problem. Offline training of the artificial neural networks (ANN) is carried out by using several combinations of the clutter parameters, with different correlation degrees. To assess the performance of the proposed estimator, we resort to Monte Carlo simulation, and we observed that it has superior performance over existing approaches in terms of estimation mean square error (MSE) and robustness to changes of the clutter correlation coefficient.

Suzuki Distributed Monostatic and Bistatic S-Band Radar Sea Clutter

Low grazing angle S-band radar sea clutter data, collected with the NetRAD multistatic radar system, are analyzed. The Suzuki distribution, a compound Gaussian model with log normal texture, is found to be a good model for both monostatic and bistatic clutter. Clutter textures from simultaneously recorded monostatic and bistatic data are interpreted using hydrodynamics, providing estimates of the water depth and wave direction.

A Data-Driven Optimization Method for Simulating Arbitrarily Distributed and Spatial–Temporal Correlated Radar Sea Clutter

A Data-Driven Optimization Method for Simulating Arbitrarily Distributed and Spatial–Temporal Correlated Radar Sea Clutter

The Prediction Model of IPIX Radar Sea Clutter Based on SSA-ELM Neural Network

The prerequisite for improving radar detection capability is to reduction of sea clutter from interfering with the target, and the accurate prediction of sea clutter is an essential prerequisite for effective suppression. To achieve an accurate prediction of sea clutter, a model for sea clutter prediction combination of the salp’s swarm algorithm, prediction using extreme learning machine after performing parameter search, which improves the prediction performance of ELM and backpropagation (BP) neural network. The convergence speed and accuracy are improved, and the overall prediction The IPIX radar sea clutter prediction reaches an accuracy of more than 99% and the prediction results of this model are better than that when only using ELM or BP neural network prediction.

Study on airborne radar sea clutter suppression using deterministic space time model in post-Doppler domain

When the airborne radar observes the land or the sea scene, the echoes will contain a large of clutter. Therefore, the target detection will be influenced and the clutter suppression is necessary. The time varying sea clutter makes the detection of sea targets more difficult. However, the airborne radar sea clutter suppression is rarely studied. In this paper, the sea clutter suppression for single channel and multi-channel are studied, respectively. The Doppler domain localization (DDL) method is used to suppress the real sea clutter data measured with a single channel. The results show that we can choose proper range cell number in the post Doppler domain to get better sea clutter suppression performance. For the multi-channel sea clutter, the clutter suppression is executed using sample matrix inverse (SMI), cross spectral metric (CSM) methods, subspace projection method and the proposed inverse sample covariance matrix (SCM) reconstruction method in the post-Doppler domain with joint three Doppler channels. It can be found that the sea clutter can be cancelled effectively by CSM and inverse SCM reconstruction methods. The residual sea clutter is relatively more for the subspace projection method due to the inaccuracy of the projection matrix. The moving targets can be detected with SMI, CSM and inverse SCM reconstruction methods, while they are submerged in the sea clutter for the subspace projection method. Meanwhile, the minimal detectable velocity (MDV) curves under different sea states show that the detection performance of the slow moving targets deteriorates with the increase of the sea state. The results can provide effective support for airborne sea clutter suppression and sea target detection.

Modeling of High-Resolution Radar Sea Clutter Using Two Approximations of the Weibull Plus Thermal Noise Distribution

Modeling of High-Resolution Radar Sea Clutter Using Two Approximations of the Weibull Plus Thermal Noise Distribution

Joint Inversion of Evaporation Duct Based on Radar Sea Clutter and Target Echo Using Deep Learning

Tropospheric duct is an anomalous atmospheric phenomenon over the sea surface that seriously affects the normal operation and performance evaluation of electromagnetic communication equipment at sea. Therefore, achieving precise sensing of tropospheric duct is of profound significance for the propagation of electromagnetic signals. The approach of inverting atmospheric refractivity from easily measurable radar sea clutter is also known as the refractivity from clutter (RFC) technique. However, inversion precision of the conventional RFC technique is low in the low-altitude evaporation duct environment. Due to the weak attenuation of the over-the-horizon target signal as it passes through the tropospheric duct, its strength is much stronger than that of sea clutter. Therefore, this study proposes a new method for the joint inversion of evaporation duct height (EDH) based on sea clutter and target echo by combining deep learning. By testing the inversion performance and noise immunity of the new joint inversion method, the experimental results show that the mean error RMSE and MAE of the new method proposed in this paper are reduced by 41.2% and 40.3%, respectively, compared with the conventional method in the EDH range from 0 to 40 m. In particular, the RMSE and MAE in the EDH range from 0 to 16.7 m are reduced by 54.2% and 56.4%, respectively, compared with the conventional method. It shows that the target signal is more sensitive to the lower evaporation duct, which obviously enhances the inversion precision of the lower evaporation duct and has effectively improved the weak practicality of the conventional RFC technique.

A Two-stage Detector for First-order Sea Clutter in HF Surface Wave Radar

The detection capabilities in vertical polarized, High-frequency SurfaceWave Radar (HFSWR) are limited by the clutter spikes in first-order sea clutter. The energy, position, and Doppler frequency of first-order sea clutter depend on the sea states, which are non-homogeneous – because of that, using a fixed expression to eliminate sea clutter gives limited results. In this paper, the authors proposed a new adaptive method using a two-stage detector based on the Order Statistic Constant False Alarm Rate (OS-CFAR) principle. The approach shows the capability to eliminate heterogeneous clutter and detect targets in the vicinities of sea clutter in the Range – Doppler map.

Challenges in radar sea clutter modelling

The ability to detect small targets in sea clutter is still an ongoing challenge, even for modern radar systems. Over the past decade, the development of new radar waveforms and signal processing algorithms has been assisted by an increasingly good understanding of the characteristics of sea clutter. Nevertheless, many challenges remain in the development of robust sea clutter models that span the wide range of sea conditions and viewing geometries encountered in practice. This paper describes the current challenges with some suggestions on how they can be overcome.

Research on sea clutter model of emulating aircraft motion based on shore-based multichannel radar

ABSTRACT Space-time adaptive processing (STAP) is an important clutter suppression technique in airborne multichannel radar. It can be a challenging problem to apply STAP to the maritime airborne radar without knowledge of sea clutter space-time coupling characteristics. The radar sea clutter data with space-time coupling characteristics can be obtained by airborne multichannel radar, but the flight testing is very expensive. A cost-effective technique is presented for emulating airborne motion based on shore-based multichannel radar. The technique is implemented by the same array shared with transmit and receive antenna, where the transmit array uses an inverse displaced phase centre antenna to emulate platform motion and the receive array is used for receiving data. Based on the above technique, a sea clutter model of aircraft motion emulation is proposed. Then, we verify the model by quantitatively comparing the number of eigenvalue and the spread width of the space-time power spectrum between the simulated and measured data. The result shows that the eigenvalue number and the spread width of 3 dB clutter ridge of measured data are more than 79.5% of that belonging to simulated data. It means that the simulated radar sea clutter data generated by this model can better fit the measured data under sea states classes 1–4, which can provide support for the analysis of sea clutter space-time characteristics.

Analysis and Simulation of Low Grazing Angle X-Band Coherent Radar Sea Clutter Using Memoryless Nonlinear Transformations

Two examples of low grazing angle radar sea clutter, both well described by the compound K-distribution model, are studied. Pulse Doppler processing is applied to obtain two dimensional range-time textures for the intensity, centroid and width of the Doppler spectrum. The first example exhibits a monochromatic swell pattern, allowing phase averaging to be applied to the textures. The second example has a more typical ocean wave spectrum. The intensity textures are gamma distributed, consistent with the compound K-distribution model, but the Doppler spectrum centroid and width textures are also found to be gamma distributed. Based on this analysis, a new method for simulation of coherent radar sea clutter is proposed, where separate memoryless nonlinear transformations are applied to a simulated water surface to generate the spatially and temporally varying intensity, centroid and width of the Doppler spectrum. The method builds on the evolving Doppler spectrum model for radar sea clutter simulation and established methods for simulation of water surfaces.

Offshore Surface Evaporation Duct Joint Inversion Algorithm Using Measured Dual-Frequency Sea Clutter

In this paper, high-precision joint inversion of evaporative duct based on dual frequency radar sea clutter data is analyzed to study the abnormal duct environment phenomenon that occurs over offshore surfaces. As the information of duct environment retrieved by radars with different frequencies is inconsistent, a joint optimization model with dynamic penalty factor is proposed, which can improve the degree of conformity between the measured clutter power and the modeled clutter power. Then, a parallel crossover quantum particle swarm optimization algorithm is used to jointly invert the objective function, which adaptively processes the inputs involved in the crossover, and effectively improves the convergence of the inversion. Compared with the single-frequency model commonly used in engineering, the average relative error of the duct height of the dual frequency joint optimization model is reduced by 3.13%, and the average relative error of the duct intensity is reduced by 6.34%, verifying the effectiveness of this method.

Parametric Modeling of Sea Clutter Doppler Spectra

For radar modeling and simulation of sea clutter, there are a number of key characteristics used to describe the observed features. These include the mean backscatter power, amplitude statistics, spatial and long-time temporal correlation, and the Doppler spectrum. Regarding the latter, a common modeling approach is to use the mean Doppler characteristics (width and center point) to relate to the sea conditions. However, this does not capture the time- and range-varying fluctuations of the Doppler spectra, which are important for assessing the behavior of coherent detection schemes. The evolving Doppler spectrum model offers a way to model these variations and can be described with a moderate number of parameters. Relating these to the observed geometry, environment and radar characteristics are important to make the models accessible. Previous work has characterized X-band radar sea clutter over a range of sea conditions and azimuth and grazing angles. In this article, new models are presented that capture variations with both the radar resolved area and integration time. This enables the evolved Doppler spectra model to be used over a much wider range of applications.