Sea Surface Target Detection Research Articles

Sea Surface Target Detection Technology: Current Status, Challenges, and Future Development Trends

Abstract As the largest geographical feature on Earth, the ocean not only harbors abundant natural resources but also serves as a crucial domain for modern military strategy. The development of sea surface target detection technology is of significant importance for maintaining maritime security, promoting the development of marine resources, and enhancing naval combat capabilities. However, compared to land target detection, sea surface target detection faces a more complex background environment and target characteristics. Sea surface targets are diverse and widely distributed, with their size, material, and motion status all posing different challenges to detection technology. This paper aims to provide an in-depth analysis of sea surface background environments, target characteristics, detection bands and methods, and target identification research status, exploring effective ways to improve the accuracy of sea surface target detection.

High-Resolution Sea Surface Target Detection Using Bi-Frequency High-Frequency Surface Wave Radar

The monitoring of the sea surface, whether it is the state of the sea or the position of targets (ships), is an up-to-date research topic. In order to determine localization parameters of ships, we propose a high-resolution algorithm for primary signal processing in high-frequency surface wave radar (HFSWR) which operates at two frequencies. The proposed algorithm is based on a high-resolution estimate of the range–Doppler (RD-HR) map formed at every antenna in the receive antenna array, which is an essential task, because the performance of the entire radar system depends on its estimation. We also propose a new focusing method allowing us to have only one RD-HR map in the detection process, which collects the information from both these carrier frequencies. The goal of the bi-frequency mode of operation is to improve the detectability of targets, because their signals are affected by different Bragg-line interference patterns at different frequencies, as seen on the RD-HR maps during the primary signal processing. Also, the effect of the sea (sea clutter) manifests itself in different ways at different frequencies. Some targets are masked (undetectable) at one frequency, but they become visible at another frequency. By exploiting this, we increase the probability of detection. The bi-frequency architecture (system model) for the localization of sea targets and the novel signal model are presented in this paper. The advantage of bi-frequency mode served as a motivation for testing the detectability of small boats, which is otherwise a very challenging task, primarily because such targets have a small radar reflective surface, they move quickly, and often change their direction. Based on experimentally obtained results, it can be observed that the probability of detection of small boats can also be significantly improved by using a bi-frequency architecture.

Sea surface target detection method based on spectrum split-peak and hurst exponent

Sea surface target detection method based on spectrum split-peak and hurst exponent

Ensemble One-Class Support Vector Machine for Sea Surface Target Detection Based on k-Means Clustering

Sea surface target detection is a key stage in a typical target detection system and directly influences the performance of the whole system. As an effective discriminator, the one-class support vector machine (OCSVM) has been widely used in target detection. In OCSCM, training samples are first mapped to the hypersphere in the kernel space with the Gaussian kernel function, and then, a linear classification hyperplane is constructed in each cluster to separate target samples from other classes of samples. However, when the distribution of the original data is complex, the transformed data in the kernel space may be nonlinearly separable. In this situation, OCSVM cannot classify the data correctly, because only a linear hyperplane is constructed in the kernel space. To solve this problem, a novel one-class classification algorithm, referred to as ensemble one-class support vector machine (En-OCSVM), is proposed in this paper. En-OCSVM is a hybrid model based on k-means clustering and OCSVM. In En-OCSVM, training samples are clustered in the kernel space with the k-means clustering algorithm, while a linear decision hyperplane is constructed in each cluster. With the combination of multiple linear classification hyperplanes, a complex nonlinear classification boundary can be achieved in the kernel space. Moreover, the joint optimization of the k-means clustering model and OCSVM model is realized in the proposed method, which ensures the linear separability of each cluster. The experimental results based on the synthetic dataset, benchmark datasets, IPIX datasets, and SAR real data demonstrate the better performance of our method over other related methods.

Maritime Target Recognition and Location System Based on Lightweight Neural Network

China’s sea surface area is vast, the need to monitor the area is too large, and the traditional human monitoring method consumes a lot of manpower. Additionally, the monitoring period is too long; the monitoring efficiency is too low; and long-term human monitoring can easily cause visual fatigue, as well as missed detection and error detection. At present, the detection of sea surface targets generally includes infrared, visible light and other different means, which can obtain the image information of sea surface targets in different ways. The infrared target detection of the sea surface can be processed in the spatial domain and frequency domain, respectively, but the image resolution is not high in general, and the detection effect is not good because it is easily affected by weather. In this paper, we propose a maritime target detection method based on embedded vision. Based on visible video images, this paper realizes the rapid detection and recognition of sea surface targets. Clouds and waves in ocean images are filtered by adding an image preprocessing module. Compared with the traditional two-frame difference method, this algorithm has better detection capability for sea surface targets. Experiments were carried out in different weather conditions to detect moving ships at sea. By comparing the number of detection boxes and the detection accuracy, the accuracy of this method reaches 90.2 percent. By designing a single camera location algorithm for the marine environment, the world coordinate location of the marine target is realized. On this basis, the communication function is added to realize the intelligent monitoring of the sea surface without human intervention.

Research on High-Resolution Reconstruction of Marine Environmental Parameters Using Deep Learning Model

The analysis of marine environmental parameters plays a significant role in various aspects, including sea surface target detection, the monitoring of the marine ecological environment, marine meteorology and disaster forecasting, and the monitoring of internal waves in the ocean. In particular, for sea surface target detection, the accurate and high-resolution input of marine environmental parameters is crucial for multi-scale sea surface modeling and the prediction of sea clutter characteristics. In this paper, based on the low-resolution wind speed, significant wave height, and wave period data provided by ECMWF for the surrounding seas of China (specified latitude and longitude range), a deep learning model based on a residual structure is proposed. By introducing an attention module, the model effectively addresses the poor modeling performance of traditional methods like nearest neighbor interpolation and linear interpolation at the edge positions in the image. Experimental results demonstrate that with the proposed approach, when the spatial resolution of wind speed increases from 0.5° to 0.25°, the results achieve a mean square error (MSE) of 0.713, a peak signal-to-noise ratio (PSNR) of 49.598, and a structural similarity index measure (SSIM) of 0.981. When the spatial resolution of the significant wave height increases from 1° to 0.5°, the results achieve a MSE of 1.319, a PSNR of 46.928, and an SSIM of 0.957. When the spatial resolution of the wave period increases from 1° to 0.5°, the results achieve a MSE of 2.299, a PSNR of 44.515, and an SSIM of 0.940. The proposed method can generate high-resolution marine environmental parameter data for the surrounding seas of China at any given moment, providing data support for subsequent sea surface modeling and for the prediction of sea clutter characteristics.

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.

Sea Surface Target Detection Using Global False Alarm Controllable Adaptive Boosting Based on Correlation Features

In the complex marine environment, traditional detectors based on the classifiers cannot guarantee the global false alarm control. In this paper, we propose a detector based on dual channel convolutional neural network (DC-CNN) and global false alarm controllable adaptive boosting tree (GFAC-A), which is shortened as DC-CNN-GFAC-A. DC-CNN focuses on the correlation features of radar echoes in time domain and frequency domain, better use multi-dimensional features and show a better feature extraction ability. Through the application of GFAC-A, the false alarm rate is introduced into the algorithm combining decision tree and AdaBoost to achieve the high-performance detection of global false alarm controllable for high-dimensional features. It is heuristics. The combination of DC-CNN and GFAC-A solves the disadvantage that current classifiers cannot meet the conditions of good performance and low false alarm. First, the connectivity information is obtained by using the frequency domain amplitude characteristics of radar echo data, and the recursive information is obtained by using the nonlinear recursive time series characteristics. And the dual-channel datasets of targets and clutter are built. Then, DC-CNN is built to extract and fuse high-dimensional features to obtain feature vectors of targets and clutter. Besides, the performance comparison of different neural network model combinations is carried out. Finally, compared with the traditional threshold-controllable classifiers, the proposed GFAC-A classifier achieves the high detection performance under the global controlled false alarm. The results show that DC-CNN-GFAC-A can achieve 96.491% detection accuracy when the false alarm rate is 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , which is superior to other detections.

Illuminator scheduling method for passive radar sea surface target detection based on RCS characteristic

ABSTRACT In order to improve the signal-to-clutter-noise ratio (SCNR) of received signal, this letter studies the illuminator scheduling problem in the detection process with the aim of maximizing SCNR time series. In order to use the illuminator with long detection time as much as possible and get high SCNR in the detection process, we propose a neighbourhood search illuminator scheduling method. Firstly, we study the characteristics of radar cross-sections (RCS) of target and sea surface under different incident directions and polarizations, and analyse the regions of high target RCS and low sea surface RCS. Then the coordinate transformation method is used to get the scheduling sequence within the detection range. Based on the global scheduling sequence, the neighbouring frames are searched to obtain the illuminator sequence of the randomly appearing target. Simulation results show that the proposed method can effectively improve the received signal SCNR 15 dB on average.

Sequence-Feature Detection of Small Targets in Sea Clutter Based on Bi-LSTM

In the complex and changeable marine environment, it is difficult to achieve high-performance detection based on statistical theory for small sea surface detectors in practical applications. In this work, a method for detecting small targets on the sea surface based on sequence-features is proposed from the perspectives of feature extraction and feature classification on a basis of real data. The sequence-feature method firstly extracts three sequence-features from the radar echo sequence, namely, the instantaneous phase, Doppler spectrum entropy, and short-time Fourier transform (STFT) marginal spectrum. The features are used to train the bidirectional long short-term memory networks (Bi-LSTM) model so that it has the ability to distinguish the target and the sea clutter. The measured dataset verifies that the proposed detector can achieve the detection of sea surface targets with controllable false alarm rate, and the performance is better than several existing detectors.

Sea-surface object detection scheme for USV under foggy environment

Sea-surface target detection is investigated for the visual image-based autonomous control of an Unmanned Surface Vessel (USV). A traditional way is to dehaze for sea-surface images in the previous target detection algorithms. However, it would cause a problem that the image dehaze performance and detection speed are difficult to be balanced. To solve the above problem, a YOLO (You Only Look Once) based target detection network with good anti-fog ability is proposed for sea-surface target detection. In this proposed method, the target detection network is trained off-line to obtain a good anti-fog ability and the target detection is performed on-line. A hazed sample generation model is built based on atmospheric single scattering inverse model to obtain sufficient samples for the off-line training in the proposed method. And then, the target detection network is trained based on the generated samples to obtain good anti-fog ability according to a new learning strategy. Finally, comparative experimental results demonstrate the effectiveness of the proposed target detection algorithm.

Detection of sea‐surface target of coastal defense radar based on Stacked Autoencoder (SAE) algorithm

In recent years, machine learning theory has set off a wave of research in the field of radar signal processing. In this study, a novel algorithm for sea-surface target detection based on a stacked autoencoder (SAE) is proposed, which has already been applied in the authors’ coastal defense radar system. In the proposed algorithm, the sea surface echo data are cut into a large number of two-dimensional (2-D) images through a sliding window, mapping the cell under test (CUT) and the 2-D images one by one and performing classification or detection from the perspective of 2-D signal processing. Experimental results of simulated data and real radar data show that the proposed algorithm based on the SAE has better target detection performance compared with the traditional cell averaging constant false alarm rate (CA-CFAR) algorithm. Besides, the proposed algorithm shows certain interference suppression ability in real data processing. As far as it is known, there is no public report on the detection of 2-D sea surface targets based on the SAE algorithm in coastal defense radar.

Study on Visual Detection Algorithm of Sea Surface Targets Based on Improved YOLOv3.

Countries around the world have paid increasing attention to the issue of marine security, and sea target detection is a key task to ensure marine safety. Therefore, it is of great significance to propose an efficient and accurate sea-surface target detection algorithm. The anchor-setting method of the traditional YOLO v3 only uses the degree of overlap between the anchor and the ground-truth box as the standard. As a result, the information of some feature maps cannot be used, and the required accuracy of target detection is hard to achieve in a complex sea environment. Therefore, two new anchor-setting methods for the visual detection of sea targets were proposed in this paper: the average method and the select-all method. In addition, cross PANet, a feature fusion structure for cross-feature maps was developed and was used to obtain a better baseline cross YOLO v3, where different anchor-setting methods were combined with a focal loss for experimental comparison in the datasets of sea buoys and existing sea ships, SeaBuoys and SeaShips, respectively. The results showed that the method proposed in this paper could significantly improve the accuracy of YOLO v3 in detecting sea-surface targets, and the highest value of mAP in the two datasets is 98.37% and 90.58%, respectively.

Anomaly Based Sea-Surface Small Target Detection Using K-Nearest Neighbor Classification

Sea-surface small target detection is always a difficult problem in high-resolution maritime ubiquitous radars for complex characteristics of sea clutter, weak target returns, and diversity of targets. Multiple features extracted from radar returns in different domains have ability but not enough to solely distinguish radar returns with target from sea clutter. Joint exploitation of multiple features becomes the key to improve detection performance. In this article, the K-nearest neighbor (KNN) algorithm and anomaly detection idea are cooperated to develop a novel sea-surface target detection method in the feature space spanned by the eight existing salient features. The detection is realized by the anomaly detection followed by a specially designed KNN-based classifier with a controllable false alarm rate. In the anomaly detection, a decision region is determined by the hyper-spherical coverage of the training set of sea clutter that is sufficient and ergodic in the feature space. The KNN-based classifier is designed based on the training sample set of sea clutter and the training sample set of simulated target returns plus sea clutter that is sufficient but nonergodic, by joint usage of feature weighting, neighbor weighting, and distance weighting. The novel method is validated by the two open and recognized IPIX and CSIR radar databases for sea-surface small target detection. The results show that it provides significant performance improvement in comparison with the existing multiple-feature-based detection methods, owing to the fact that the novel method avoids the dimension restriction and feature compression loss in the existing methods.

Method for weak target detection in sea environment

In this study, a new method for sea surface target detection in low signal-to-clutter environment is proposed, which can not only reduce the computation amount but also suppress the sea clutter, static targets and noise. Firstly, the authors use a more general echo signal model, which is more matching for the sea environment. Next, a fast estimation technique with few searching times to reduce the computation of the detection process is put forward. Then, a canceller is designed, which has the merit of suppressing the strong sea clutter, static targets, and noise. Finally, the experiment results demonstrate that the proposed method can achieve high detection probability, fast estimation, and strong suppression ability.

Sea Clutter Suppression for Shipborne HF Radar Using Cross-Loop/Monopole Array

The shipborne high-frequency ground wave radar (HFGWR) provides a better condition for sea surface target detection than the shore-based one because of its maneuverability and flexibility. However, the first-order Bragg spectra of the shipborne HFGWR would be broadened by the Doppler shift of the ship motion, which makes the vessel echo easily submerged. Even though some methods have been applied to suppress the broadened Bragg spectra for target detection, most of them are hard to achieve good result in a small-aperture radar. An orthogonal projection method to suppress the broadened Bragg spectra using a cross-loop/monopole array is proposed in this letter. The proposed method mainly includes two steps: 1) estimate the space of sea clutter in a spatial domain according to the ocean echo characteristics and 2) design an adaptive filter of single or multiple Doppler bins on the estimated clutter space to suppress the first-order spectra. After suppression, we use the multiple signal classification algorithm to estimate the incident direction of targets. The experimental data validate that the sea clutter is well suppressed and the target signal-to-clutter ratio is increased by the algorithm.

Decision Tree Based Sea-Surface Weak Target Detection With False Alarm Rate Controllable

Aiming at accurate weak sea-surface target detection, this letter devotes to designing a learning-based detector that can work well even in varying detection environments. We first exploit the concept of the fractal theory to extract three representative features in the time and frequency domains and construct a three-dimensional feature space. We then combine the constructed feature space with the decision tree approach to design an environment-adaptive detector. Most importantly, we modify the decision tree based detector to an FAR-controllable detector to meet the requirements of different detection applications. Experimental results demonstrate that, compared with existing detectors, our proposed detector improves the detection probability in both low FAR (up to 35%) and low SCR cases (up to 55%).

Sea surface target detection based on complex ARMA-GARCH processes

In financial applications, it is common practice to fit return series by AutoRegressive Moving-Average (ARMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. In this paper, we develop a complex-valued ARMA-GARCH model for the sea clutter modeling application. Compared with the AR-GARCH model, the additionally introduced MA terms make the proposed model capable of considering the dependence of conditional variances of adjacent echo measurements as model coefficients, improving the modeling precision by taking advantage of the strong correlations between adjacent measurements. Based on the complex-valued ARMA-GARCH process for sea clutter modeling, we further develop a sea surface target detection algorithm. By analyzing a large number of the practical sea clutter data, we evaluate its performance and show that the proposed sea surface target detector offers a noticeable improvement for the probability of detection, comparing with the state-of-the-art AR-GARCH detector.

Targets Detection Based on Multifractal Method

Mean sea clutter scattering coefficient model can reflect the relationship between the scattering coefficient of sea clutter and radar parameters and environmental factors, commonly used at sea clutter radar range forecasts. Sea clutter amplitude distribution model is essentially in the form of a probability density function to describe the magnitude of the statistical properties of sea clutter. In this paper, based on the sea surface target detection process uncertain information based on multiple objective study of fractal theory, simulation results show that our detection methods to get good results, you can improve the reliability and intelligence of detection.

High-order fractal characterisation of sea-scatteredsignals and detection of sea-surface targets

A novel method for identifying radar targets and clutter is presented. Instead of the traditional fractal dimension, the lacunarity, which is a high-order fractal feature, is used in this method. The formula for lacunarity in additive noise is also given. The experiments show that higher detection accuracy can be obtained by using the lacunarity feature than by using the fractal dimension.