Search Results (57)

Maritime search and rescue (SAR) demands reliable sensing and communication under sea clutter. Emerging integrated sensing and communication (ISAC) technology provides new opportunities for the development and modernization of maritime radio communication, particularly in relation to search and rescue. This study investigated the dual-function capability of a phase-coded frequency modulated continuous wave (FMCW) system for search and rescue at sea, in particular for life signs detection in the presence of sea clutter. The detection capability of the FMCW system was enhanced by applying phase-modulated codes on chirps, and radar-centric communication function is supported simultaneously. Various phase-coding schemes including Barker, Frank, Zadoff-Chu (ZC), and Costas were assessed by adopting the peak sidelobe level and integrated sidelobe level of the ambiguity function of the established signals. The interplay of sea waves was represented by a compound K-distribution model. A multiple-input multiple-output (MIMO) architecture with the ZC code was adopted to detect multiple objects with a high resolution for micro-Doppler determination by taking advantage of spatial coherence with beamforming. The effectiveness of the proposed method was validated on the 4-transmit, 4-receive (4 × 4) MIMO system with ZC coded FMCW signals. Monte Carlo simulations were carried out incorporating different combinations of targets and user configurations with a wide range of signal-to-noise ratio (SNR) settings. Extensive simulations demonstrated that the mean squared error (MSE) of range estimation remained low across the evaluated SNR setting, while communication performance was comparable to that of a baseline orthogonal frequency-division multiplexing (OFDM)-based system. The high performance demonstrated by the proposed method makes it a suitable maritime search and rescue solution, in particular for vision-restricted situations. Full article

This paper proposes a new carrier domain approach to suppress spreading first-order sea clutter in shipborne passive radar systems using Digital Radio Mondiale (DRM) signals as illuminators. The DRM signal is a broadcast signal that operates in the high-frequency (HF) band and employs orthogonal frequency-division multiplexing (OFDM) modulation. In shipborne DRM-based passive radar, sea clutter sidelobes elevate the noise level of the clutter-plus-noise covariance matrix, thereby degrading the target signal-to-interference-plus-noise ratio (SINR) in traditional space–time adaptive processing (STAP). Moreover, the limited number of space–time snapshots in traditional STAP algorithms further degrades clutter suppression performance. By exploiting the multi-carrier characteristics of OFDM, this paper proposes a novel algorithm, termed Space Time Adaptive Processing by Carrier (STAP-C), to enhance clutter suppression performance. The proposed method improves the clutter suppression performance from two aspects. The first is removing the transmitted symbol information from the space–time snapshots, which significantly reduces the effect of the sea clutter sidelobes. The other is using the space–time snapshots obtained from all subcarriers, which substantially increases the number of available snapshots and thereby improves the clutter suppression performance. In addition, we combine the proposed algorithm with the dimensionality reduction algorithm to develop the Joint Domain Localized-Space Time Adaptive Processing by Carrier (JDL-STAP-C) algorithm. JDL-STAP-C algorithm transforms space–time data into the angle–Doppler domain for clutter suppression, which reduces the computational complexity. Simulation results show the effectiveness of the proposed algorithm in providing a high improvement factor (IF) and less computational time. Full article

Radar target detection in a sea clutter environment is of significant importance in both civilian and military applications, with the detection of small maneuvering targets being particularly challenging. To address this issue, this paper introduces the autocorrelation characteristics of sea clutter into orthogonal projection operations to suppress sea clutter and enhance the detection capability of small maneuvering targets on the sea surface. The proposed method first generates speckle components that are consistent with the correlation characteristics of the observed sea clutter. Then, it uses these speckle components to derive the feature subspace of the sea clutter and applies this subspace in an orthogonal projection suppression algorithm, thereby achieving effective suppression of the sea clutter. This method does not rely on the covariance matrix estimation of sea clutter from reference cells but instead directly utilizes the autocorrelation characteristics of the observed sea clutter data to obtain the feature subspace, making it more adaptable to different environments. Simulation and experimental results demonstrate that this method significantly suppresses sea clutter and effectively improves the performance of target detection on the sea surface. Full article

The non-Gaussian nature of radar-observed clutter echoes induces performance degradation in the context of remote sensing target detection when using conventional Gaussian detectors. To enhance target detection performance, this study addresses the issue of adaptive detection in nonzero-mean non-Gaussian sea clutter environments. The nonzero-mean compound Gaussian model, composed of the texture and complex Gaussian speckle, is utilized to capture the sea clutter. Further, we adopt the inverse Gamma, Gamma, and inverse Gaussian distributions to characterize the texture component. Novel adaptive detectors based on the two-step Rao and Wald tests, taking advantage of the maximum a posteriori (MAP) method to estimate textures, are designed. More specifically, test statistics of the proposed Rao- and Wald-based detectors are derived by assuming the speckle covariance matrix (CM), mean vector (MV), and clutter texture in the first step. Then, the sea clutter parameters assumed to be known are replaced with their estimations, and fully adaptive detectors are obtained. The Monte Carlo performance evaluation experiments using both simulated and measured sea clutter data are conducted, and numerical results validate the constant false alarm rate (CFAR) properties and detection performance of the proposed nonzero-mean detectors. Additionally, the proposed Rao and Wald detectors, respectively, show strong robustness and good selectivity for mismatch signals. Full article

Detection and tracking of marine weak targets can be effectively solved by track-before-detect (TBD) algorithms based on particle filtering. However, these algorithms are susceptible to influence from neighboring targets, leading to potential issues like misassociation and tracking failure. In this paper, an auxiliary particle flow track-before-detect algorithm designed for marine neighboring weak targets is proposed which can effectively track marine neighboring weak targets under long-tail sea clutter. Firstly, marine neighboring targets are modeled by the generalized Pareto model, and an offline lookup table is utilized to obtain a non-closed solution, decreasing calculation cost. Subsequently, prediction is employed to classify targets, and measurement information is iteratively used to determine the sequence of target updates, effectively suppressing influence from neighboring targets. Finally, particles with higher measurement energy are chosen, and the Geodesic particle flow is employed to guide the particles toward better importance distribution, which enhances the accuracy of target trajectory estimation. Simulation experiments indicate that compared with track-before-detect algorithms based on parallel partition (PP) and auxiliary parallel partition (APP), the proposed algorithm shows an increase of 43.1% and 25.8% in detection probability at 6 dB, and a reduction of 76.6% and 66.2% in Root Mean Square Error (RMSE). Detection ability and trajectory estimation performance are effectively improved in the simulation, and excellent tracking performance is also confirmed in real clutter experiments. Full article

Maintaining a Constant False Alarm Rate (CFAR) in the presence of K-distributed sea clutter is vital due to the dynamic and unpredictable nature of maritime environments. However, conventional CFAR detectors suffer significant performance degradation in multi-target scenarios, primarily due to the masking effect caused by interfering targets. To address this challenge, this paper introduces an advanced detection scheme that integrates Linear Density-Based Spatial Clustering for Applications with Noise (Lin-DBSCAN) with CFAR processing. Lin-DBSCAN is specifically tailored to efficiently identify and isolate interfering targets and sea spikes, which typically manifest as outliers in the symmetric reference windows surrounding the Cell Under Test (CUT). By leveraging Lin-DBSCAN, the proposed Lin-DBSCAN-CFAR method effectively filters out anomalous signals from the background clutter, resulting in enhanced detection accuracy and robustness, especially under complex sea clutter conditions. Extensive simulations under varying conditions, including multiple target environments, varying false alarm rates, and different clutter shape parameters, demonstrate that Lin-DBSCAN-CFAR significantly outperforms conventional CFAR approaches. It is noteworthy that the proposed method achieves detection performance comparable to the more computationally intensive DBSCAN-CFAR while significantly reducing computational complexity. Simulation results reveal that Lin-DBSCAN-CFAR requires a 1 to 2 dB lower SNR to reach a detection probability of 0.8 compared with the nearest traditional CFAR techniques, confirming its superiority in both accuracy and efficiency. Full article

In this article, the problem of Bayesian detecting rank-one distributed targets under subspace interference and compound Gaussian clutter with inverse Gaussian texture is investigated. Due to the clutter heterogeneity, the training data may be insufficient. To tackle this problem, the clutter speckle covariance matrix (CM) is assumed to obey the complex inverse Wishart distribution, and the Bayesian theory is utilized to obtain an effective estimation. Moreover, the target echo is assumed to be with a known steering vector and unknown amplitudes across range cells. The interference is regarded as a steering matrix that is linearly independent of the target steering vector. By utilizing the generalized likelihood ratio test (GLRT), a Bayesian interference-canceling detector that can work in the absence of training data is derived. Moreover, five interference-cancelling detectors based on the maximum a posteriori (MAP) estimate of the speckle CM are proposed with the two-step GLRT, the Rao, Wald, Gradient, and Durbin tests. Experiments with simulated and measured sea clutter data indicate that the Bayesian interference-canceling detectors show better performance than the competitor in scenarios with limited training data. Full article

The analysis of marine environmental parameters plays an important role in areas such as sea surface simulation modeling, analysis of sea clutter characteristics, and environmental monitoring. However, ocean observation remote sensing satellites typically deliver large volumes of data with limited spatial resolution, which often does not meet the precision requirements of practical applications. To overcome challenges in constructing high-resolution marine environmental parameters, this study conducts a systematic comparison of various interpolation techniques and deep learning models, aiming to develop a highly effective and efficient model optimized for enhancing the resolution of marine applications. Specifically, we incorporated adaptive global attention (AGA) mechanisms and a spatial gating unit (SGU) into the model. The AGA mechanism dynamically adjusts the weights of different regions in feature maps, enabling the model to focus more on critical spatial features and channel features. The SGU optimizes the utilization of spatial information by controlling the information transmission pathways. The experimental results indicate that for four types of marine environmental parameters from ERA5, our model achieves an overall PSNR of 44.0705, an SSIM of 0.9947, and an MAE of 0.2606 when the resolution is increased by a upscale factor of 2, as well as an overall PSNR of 35.5215, an SSIM of 0.9732, and an MAE of 0.8330 when the resolution is increased by an upscale factor of 4. These experiments demonstrate the model’s effectiveness in enhancing the spatial resolution of satellite-derived marine environmental parameters and its ability to be applied to any marine region, providing data support for many subsequent oceanic studies. Full article

Sea clutter introduces a significant amount of non-target reflections in the echo signals received by radar, complicating target detection and identification. To address the challenge of existing filter parameters being unable to adapt in real-time to the characteristics of sea clutter, this paper integrates ocean numerical models into the sea clutter spectrum estimation. By adjusting filter parameters based on the spectral characteristics of sea clutter, the accurate suppression of sea clutter is achieved. In this paper, the Weather Research and Forecasting (WRF) model is employed to simulate the ocean dynamic parameters within the radar detection area. Hydrological data are utilized to calibrate the parameterization scheme of the WRF model. Based on the simulated ocean dynamic parameters, empirical formulas are used to calculate the sea clutter spectrum. The filter coefficients are updated in real-time using the sea clutter spectral parameters, enabling precise suppression of sea clutter. The suppression algorithm is validated using X-band radar-measured sea clutter data, demonstrating an improvement factor of 17.22 after sea clutter suppression. Full article

An intelligent approach is proposed and investigated in this paper for the detection of ultra-low-altitude sea-skimming moving targets for airborne pulse Doppler radar. Without suppressing interferences, the proposed method uses both target and multipath information for detection based on their distinguishable image features and deep learning (DL) techniques. First, the image features of the target, multipath, and sea clutter in the real-measured range-Doppler (RD) map are analyzed, based on which the target and multipath are defined together as the generalized target. Then, based on the composite electromagnetic scattering mechanism of the target and the ocean surface, a scattering-based echo generation model is established and validated to generate sufficient data for DL network training. Finally, the RD features of the generalized target are learned by training the DL-based target detector, such as you-only-look-once version 7 (YOLOv7) and Faster R-CNN. The detection results show the high performance of the proposed method on both simulated and real-measured data without suppressing interferences (e.g., clutter, jamming, and noise). In particular, even if the target is submerged in clutter, the target can still be detected by the proposed method based on the multipath feature. Full article

The development of radar systems requires extensive testing. However, field experiments are costly and time-consuming. Sea clutter simulation is of great significance for evaluating radar system detection performance. Traditional clutter simulation methods are unable to achieve clutter simulation based on the description of environmental parameters, which leads to a certain gap from practical applications. Therefore, this paper proposes a sea clutter simulation method based on the deep cognition of characteristic parameters. Firstly, the proposed method innovatively constructs a shared multi-task neural network, which compensates for the lack of integrated prediction of multi-dimensional characteristic parameters of sea clutter. Furthermore, based on the predicted clutter characteristic parameters combined with the spatial–temporal correlated K-distribution clutter simulation method, and considering the modulation of radar antenna patterns, the whole process of end-to-end simulation from measurement condition parameters to clutter data is accomplished for the first time. Finally, four metrics are cited for a comprehensive evaluation of the simulated clutter data. Based on the experimental results using measured data, the data simulated by this method have a correlation of over 93% in statistical characteristics with the measured data. The results demonstrate that this method can achieve the accurate simulation of sea clutter data based on measured condition parameters. Full article

In this study, we propose a novel method for retrieving the three-dimensional (3D) wave surface from sea clutter using both simulated and measured data. First, the linear wave superposition model and modulation principle are employed to generate simulated datasets comprising 3D wave surfaces and corresponding sea clutter. Subsequently, we develop a Pix2Pix model enhanced with a self-attention mechanism and a multiscale discriminator to effectively capture the nonlinear relationship between the simulated 3D wave surfaces and sea clutter. The model’s performance is evaluated through error analysis, comparisons of wave number spectra, and differences in wave surface reconstructions using a dedicated test set. Finally, the trained model is applied to reconstruct wave surfaces from sea clutter data collected aboard a ship, with results benchmarked against those derived from the Schrödinger equation. The findings demonstrate that the proposed model excels in preserving high-frequency image details while ensuring precise alignment between reconstructed images. Furthermore, it achieves superior retrieval accuracy compared to traditional approaches, highlighting its potential for advancing wave surface retrieval techniques. Full article

The modeling and analysis of sea clutter are of great significance in radar target detection studies in marine environments. Sea clutter typically exhibits non-Gaussian characteristics and spatiotemporal correlations, posing challenges for modeling, especially when generating simulation data of continuous correlated non-Gaussian random processes. This paper proposes a novel method for sea clutter modeling. First, feature description functions are constructed to individually characterize the amplitude, temporal, and spatial correlations of sea clutter, allowing for an accurate depiction of its characteristics with fewer parameters. Subsequently, simulation data are generated based on these feature description functions, satisfying the amplitude distribution, temporal correlation, and spatial correlation characteristics of sea clutter. Additionally, complex signal forms are introduced in the underlying signal processing to generate texture and speckle components of sea clutter, enhancing the alignment of simulation data with actual data. Through comparison with measured sea clutter data, the proposed method has been shown to accurately simulate complex sea clutter with real-world characteristics. Full article

Compound-Gaussian models (CGMs) are widely used to characterize sea clutter. Various types of texture distributions have been developed so that the CGMs can cover sea clutter in different conditions. In this paper, the Weibull distributions are used to model textures of sea clutter, and the CGM with Weibull-distributed textures is used to derive the CGWB distributions, a new type of biparametric distribution. Like the classic K-distributions and Compound-Gaussian with lognormal texture (CGLN) distributions, the biparametric CGWB distributions without analytical expressions can be represented by the closed-form improper integral. Further, the properties of the CGWB distributions are investigated, and four moment-based estimators using sample moments, fractional-order sample moments, and generalized sample moments are given to estimate the parameters of the CGWB distributions. Their performance is compared by simulated clutter data. Moreover, measured sea clutter data are used to examine the suitability of the CGWB distributions. The results show that the CGWB distributions can provide the best goodness-of-the-fit for low-resolution sea clutter data as alternatives to the classic K-distributions. Full article

This paper addresses polarimetric adaptive coherent detection of radar targets embedded in sea clutter. Initially, radar clutter data across multiple polarimetric channels is modeled using a compound Gaussian framework featuring an unspecified speckle covariance matrix and lognormal texture distribution. Subsequently, three adaptive polarimetric coherent detectors are derived, employing parameter estimation and two-step versions of the generalized likelihood ratio test (GLRT): the complex parameter Rao and Wald tests. These detectors utilize both clutter texture distribution information and radar data’s polarimetric aspects to enhance detection performance. Simulation experiments demonstrate the superiority of three proposed detectors over the competitors, and that they are sensitive to polarimetric channel parameters such as secondary data quantity, target or clutter speckle correlation, and signal-to-clutter ratio disparity. Additionally, the proposed detectors exhibit a near-constant false alarm rate relative to average clutter power and speckle covariance matrix. Full article