Convolutional neural network (CNN)-based detectors for small targets on the sea surface have proven effective, yet their increasingly complex structures and high energy demands pose challenges to deployment on resource-limited devices. To address this issue, this letter proposes a residual spiking network (RSN), which is developed within the advanced framework of spiking neural networks (SNNs). By using spiking neurons (SNs) as core computational units, the RSN can efficiently transmit features extracted from time-frequency graphs (TFGs) through discrete spikes in the backbone network. Meanwhile, the output is derived from the membrane voltage of the SNs, enabling reliable control over false alarms. Experimental results from the IPIX dataset validate the RSN’s stability in controlling the probability of false alarms (PFAs). With an observation time of 1.024 s and a PFA of 0.001, the average probability of detection (PD) is 0.8266. The RSN demonstrates a PD comparable to that of ResNet18 while consuming only 70% of its energy, highlighting its potential for practical applications in resource-constrained environments.

This letter addresses the problem of adaptive coherent detection of maritime high-resolution radar range-spread targets in correlated heavy-tailed sea clutter. We first model radar sea clutter by the compound Gaussian model with lognormal texture and unknown speckle covariance matrix. The lognormal-distributed texture can capture the tail-level of sea clutter, and the speckle covariance matrix contains the pulse-to-pulse correlation of sea clutter. Then, based on the two-step generalized likelihood ratio test and the maximum likelihood or a posteriori estimation of unknown parameters, an adaptive coherent generalised likelihood ratio test with lognormal texture detector is proposed to detect radar range-spread targets. The proposed detector has the ability to be adaptive to clutter power mean, non-Gaussianity and pulse-to-pulse correlation. The performance evaluation experiments on simulated and measured data show that the proposed detector outperforms conventional adaptive detectors. More specifically, the detection results on measured data indicate that when the number of target range cells is 3 and the probability of detection reaches 0.8, the proposed detector has a signal-to-clutter ratio gain of about 1 dB over its competitors.

This letter deals with the knowledge-aided adaptive detection problem of radar targets in non-homogenous correlated compound Gaussian sea clutter. The lognormal distribution is used as the prior distribution of the clutter texture to match the non-Gaussianity of sea clutter. In addition, in order to ensure the estimation accuracy of the covariance matrix structure of sea clutter, a convex combination estimator (CCE) is proposed by jointly exploiting the prior information and the current secondary data. Then, a knowledge-aided adaptive detector is designed on the basis of the complex parameter Gradient test and the CCE. Numerical experiments verify the effectiveness of the proposed CCE and adaptive detector in comparison with their counterparts, respectively.

Sea surface target detection with Graph Neural Networks (GNN) is an emerging method. However, the correlation information of radar returns cannot be efficiently exploited by the conventional Graph Convolutional Network (GCN). Therefore, this paper proposes a small floating target detection method based on graph attention network (GAT) with spatio-temporal correlation of clutter maps, and designs fusion weighting and dynamic attention mechanism for practical problem. First, the dwell radar data is modeled as a graph structure according to its spatio-temporal information. The proposed graph structure allows Doppler spectra of same-type samples to be accumulated separately for sea clutter and target returns. Then, we propose a GAT-based detector and optimize it to create variants: GATv2, GAT-Fused, and GATv2-Fused. These variants aim to reduce sea spike interference and jointly utilize spatio-temporal clutter map information and feature correlation. Both measured and simulated data demonstrate that the proposed attention-based detectors effectively identify marine small floating targets, including out-of-distribution (OOD) detection, outperforming conventional feature-based detectors, the GCN detector, and the pure GAT detector.

In modern radar applications, data-driven sea-land segmentation of complex environments based directly on radar returns without the help of electronic charts and other information has attracted researchers’ attention due to the solidification of exogenous information and defect of non-real timeness. However, the sea-land segmentation based on radar echoes still faces two problems: First, complex clutter environment with mixed ground and sea clutter leads to a large dynamic range of clutter power, which makes power-based segmentation unreliable; Second, stable segmentation features are difficult to obtain when echoes in a single scan period are limited. In the case of multi-frame radar echoes, this paper first unwraps the phases of two adjacent echo sequences and extracts a new similarity measure for sea-land segmentation based on the covariance of phase difference sequences between sea clutter and ground clutter. Then, inspired by the idea of iteration, this paper proposes a method to iterate the covariance matrix by iterating the clutter map of the characteristic differences of multi-frame echoes to distinguish between sea and ground clutter more effectively. Experimental results based on the measured data show that the proposed sea-land segmentation method based on multi-frame echoes can effectively separate sea and land areas and ensure the quality of the segmentation results. And after several consecutive scan periods, the proposed sea-land segmentation method is more accurate and robust than other sea-land segmentation methods.

This article presents a classification method to classify different marine floating small targets, which can realize effective classification of different targets in strong clutter background. The design of proposed classification method is primarily based on block-whitened time–frequency spectrogram and pre-trained convolution neural network (CNN). Block-whitening clutter suppression is used to process target echoes. By converting a strong clutter background to an approximately noisy background, the effect of strong clutter on classification is reduced. Then, the time–frequency spectrogram of targets is extracted from the block-whitened target echoes, which converts a signal in time domain into a time–frequency spectrogram with more information. In addition, the block-whitened time–frequency spectrograms are input to a pre-trained CNN for feature extraction and classification training. By exploiting pre-training procedure, the proposed method can effectively classify different marine floating small targets and solve the problem of limited target samples in practical applications. Finally, a dataset of three kinds of measured maritime radar targets is constructed to verify the effectiveness of proposed method. Experimental results show that compared with competitors, the pre-trained CNN with block-whitened time–frequency spectrograms can achieve higher performance on the measured dataset.

In this paper, we consider the detection problem of marine radar targets embedded in correlated non-Gaussian sea clutter, which is modelled by a compound Gaussian model with lognormal texture (CG-LN) and unknown covariance matrices. In order to reduce the dependence of detectors on training data, the original radar data are transformed via exploiting the persymmetric structure of clutter covariance matrix. Then, four complex parameter suboptimum tests, which are the Rao, Wald, gradient, and Durbin tests, are utilized to design the adaptive persymmetric coherent detectors for radar targets. It is shown that the Gradient test and the Durbin test coincide with the Rao test for the problem of radar target detection in CG-LN sea clutter. We prove that the proposed persymmetric Rao detector with lognormal texture (PRAO-LND) and persymmetric WALD detector with lognormal texture (PWALD-LND) can ensure the constant false alarm rate with respect to the clutter power mean and the clutter speckle covariance matrix. Experimental results on simulated and measured radar data show that the proposed PRAO-LND performs better than its competitors, and is robust to the mismatched signals. Moreover, the proposed PWALD-LND has asymptotic performance with the proposed PRAO-LND, when the non-Gaussianity of sea clutter is weakened, and has good selectivity with signal mismatch.

In this paper, we consider the problem of robustly and accurately estimating the shape parameters ofK-distributed sea clutter in the maritime radar industry. Outliers formed by non-sea-surface echoes have a significant negative impact on the estimation accuracy. To improve the estimation performance, we first propose a bipercentiles feedforward neural network for the shape parameter$\eta$(BP-FFNN-$\eta$), which utilizes a ratio of two percentiles and a two-hidden-layer feedforward neural network. The BP-FFNN-$\eta$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. Moreover, to solve 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-$\eta$), which consists of multiple BP-FFNNs-$\eta$and a multi-class classification network. The MBP-FFNN-$\eta$can perceive the change in the proportion of outliers, so an accurate estimate can be obtained from an unaffected BP-FFNN-$\eta$. Finally, training and test data are constructed to train and evaluate the proposed methods, respectively. Experimental results demonstrate that the BP-FFNN-$\eta$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-$\eta$avoids table lookups, and produces a continuous estimate. The MBP-FFNN-$\eta$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.

This letter presents a generalized likelihood ratio test (GLRT)-based adaptive detector specially for the sub-Gaussian symmetric alpha-stable (SGS$\alpha \text{S}$) sea clutter background. Since the probability density function (PDF) of SGS$\alpha \text{S}$distribution cannot be expressed as a closed-form expression in terms of the elementary function, the research on target detectors in alpha-stable clutter background is very limited. In this letter, firstly, the Fox’s H-function is adopted to formulate the PDF of the SGS$\alpha \text{S}$distribution so that the PDF of SGS$\alpha \text{S}$distribution can be expressed as a closed form in terms of H-function. Then, the GLRT-based detector in alpha-stable clutter is designed based on the two-step GLRT criterion and the closed-form expression of the test statistics in terms of the H-function is given. Experimental results based on the simulated data and the measured data verify the effectiveness of the proposed detector.

We consider the detection problem of maritime radar targets in the training-sample-starved and non-Gaussian sea clutter environment. The performance of conventional detectors for radar targets is seriously degraded due to both the starvation of training samples for estimating the clutter covariance matrix and the non-Gaussianity of sea clutter. In this letter, we adopt the inverse Gaussian distribution and the inverse complex Wishart distribution to model the texture and speckle covariance matrix of sea clutter, respectively. Then an adaptive Bayesian detector is developed based on the two-step generalized likelihood ratio test and the maximum posterior estimates of clutter parameters. Finally, the experimental results on simulated and measured data demonstrate the performance superiority of the proposed detector over its competitors, especially when the training samples are starved.

This article deals with the problem of detecting a marine target with coherent radars in a correlated heavy-tailed sea clutter background. The heavy-tailed sea clutter is modeled by a compound-Gaussian model, and the clutter texture is characterized by the lognormal distribution with a new parameterization form. We develop an adaptive coherent detector on the basis of the two-step generalized likelihood ratio test. The proposed detector can achieve adaptation to sea clutter characteristics by using the maximum a posterior estimate of the clutter texture, the constrained approximate maximum likelihood estimator of the speckle covariance matrix, and the proposed negative- and positive-fractional moment estimate of amplitude parameters of sea clutter. Remarkably, the proposed detector inherently ensures a constant false alarm rate with respect to the clutter power mean and the speckle covariance matrix. Finally, numerical experiments using simulated data and real radar data demonstrate that the proposed estimator and adaptive coherent detector outperform their respective competitors.

Non-Gaussian sea clutter causes conventional detectors designed in Gaussian clutter to suffer detection performance degradation, and some nuisance parameters cause the uniformly most powerful test to be unavailable. To improve the detection performance of maritime radar targets, we investigate the design of adaptive detectors in correlated non-Gaussian sea clutter via using suboptimal tests. The non-Gaussian sea clutter is modelled as a product of lognormal-distributed texture and complex Gaussian speckle. Two adaptive radar target detectors are developed by using the suboptimal two-step Wald and Rao tests. Specifically, a non-adaptive detector is derived by the Wald or Rao test when the clutter texture and speckle covariance matrix are assumed to be known in the first step; then the clutter parameters known in the first step are estimated, and the true parameters of the detector obtained in the first step are replaced with the estimated values. Theoretical proof and experimental verification indicate that the two proposed detectors have the constant false alarm property with regard to the clutter speckle covariance matrix and the clutter average power. Numerical results on simulated and measured radar data show that the proposed Rao-based detector outperforms its competitors, and has the stronger robustness to the signal mismatch compared to the proposed Wald-based detector.

This article addresses the detection problem of radar targets embedded in nonhomogeneous and non-Gaussian sea clutter. Nonhomogeneity leads to insufficiency of secondary data for estimating the clutter speckle covariance matrix, and non-Gaussianity causes sea clutter to become spiky. In this article, the persymmetry of the clutter covariance matrix is adopted to alleviate the requirement of secondary data, and the prior distribution of clutter texture is exploited to tackle the clutter non-Gaussianity. Based on such clutter knowledge, three adaptive detectors are proposed according to the principles of the generalized likelihood ratio test, the Wald test, and the Rao test. It is proven that three detectors ensure constant false alarm rate (CFAR) properties with respect to both the clutter speckle covariance matrix and the clutter power mean. Simulation experiments show that three detectors outperform their competitors.

Radar external calibration is an important measurement in determining the radar cross sections of unknown targets and the reflectance coefficients of the sea surface. This measurement is often made in an anechoic chamber by standard programs. Sometimes, this measurement must be made in outdoor conditions, and the ambient environment severely affects the calibration precision. Thus, the scheme of the measurement must be specially designed. In this article, a calibration conducted at the sea near Lingshan Island is reported, and a full program of an external calibration on the sea surface and relevant analysis are presented for a P-band island-based sea clutter measurement radar. Data analysis shows that the multipath effect from the sea surface is a major factor to degrade the precision of the calibration. Measurement data over 4 days and the multipath reflectance model of the sea surface are combined to estimate the power increment from the multipath effect. Radar system constant is estimated based on the estimated power increments at different sites, where the viewing geometries of the radar are different. The results show that the proposed external calibration method on the sea surface attains a satisfactory precision.

In this letter, we focus on the statistical modeling of sea clutter amplitudes. Due to its non-Gaussian nature, the existing statistical models are sometimes difficult to represent well the heavy-tailed portion of amplitude distribution. To address this problem, we propose a compound Gaussian (CG) model with a generalized inverse Gaussian (GIG) texture to describe sea clutter amplitudes. In this regard, the probability density function and the cumulative distribution function of the clutter amplitudes for the proposed model are derived. Moreover, we provide an approach to estimate the unknown parameters of the proposed CG-GIG distribution. The experimental results indicate that the CG-GIG distribution is more suitable to describe the amplitudes of non-Gaussian sea clutter than its competitors.

This letter addresses the feature detection design for the target embedded in sea clutter. Three polarization features (the relative surface scattering power, the relative volume scattering power, and the relative dihedral scattering power) are obtained based on the observed multipolarization channel returns. Then, 3-D feature detector is constructed to detect the sea-surface small floating target based on the three polarization features. The experiments based on the measured radar data show that the proposed method attains better detection performance and better robustness than do several existed feature-class detectors.

The conventional methods for target detection and discrimination in high-resolution synthetic aperture radar (SAR) images usually have low accuracy and slow speed, especially for large complex scenes. To overcome these drawbacks, in this paper, we propose a target detection and discrimination method based on visual attention model. In the detection stage, to pop out the targets and suppress the background clutter in the saliency map, we select the task-dependent scales from the Gaussian pyramid of the original SAR image. Moreover, we adopt the clustering algorithm to remerge several isolated focus of attention areas, which are obtained from the saliency map, into a complete target region. The candidate target SAR image chips are extracted with relative high accuracy and low time cost in this stage. Since there may be single target, multiple targets, or partial targets with complex clutter in each SAR image chip, it is hard to acquire accurate target-shaped blob via segmentation. Some classical discrimination features which are extracted based on target segmentation may lose effectiveness. In the discrimination stage of our method, to solve the above problem, based on the saliency and gist (SG) features for optical satellite images, we propose the modified SG (MSG) features for SAR target discrimination. The MSG features are complementary to each other and can provide a more complete description of the extracted SAR image chips without segmentation, which also reduces the computation burden. The experimental results on the synthetic images and miniSAR real SAR image data set demonstrate that the proposed target detection and discrimination method can detect and discriminate the targets from the complex background clutter with high accuracy and fast speed in high-resolution SAR images.

A novel non‐coherent detection scheme for range‐spread targets in the sea clutter is developed in this study. The detector utilises the bi‐window non‐linear shrinkage map (BNSM) to reduce sea clutter while preserving target echoes before energy integration (EI). The EI detector based on BNSM (EI‐BNSM) is compared with the conventional non‐coherent constant false alarm ratio detectors based on measured data, and the good performance of EI‐BNSM detector is reported.