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Journal articles on the topic 'Sea clutter extraction'
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1
Zhang, Le, Anke Xue, Xiaodong Zhao, Shuwen Xu, and Kecheng Mao. "Sea-Land Clutter Classification Based on Graph Spectrum Features." Remote Sensing 13, no. 22 (November 15, 2021): 4588. http://dx.doi.org/10.3390/rs13224588.
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In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.
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Zhang, Ling, Wei You, Q. Wu, Shengbo Qi, and Yonggang Ji. "Deep Learning-Based Automatic Clutter/Interference Detection for HFSWR." Remote Sensing 10, no. 10 (September 21, 2018): 1517. http://dx.doi.org/10.3390/rs10101517.
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High-frequency surface wave radar (HFSWR) plays an important role in wide area monitoring of the marine target and the sea state. However, the detection ability of HFSWR is severely limited by the strong clutter and the interference, which are difficult to be detected due to many factors such as random occurrence and complex distribution characteristics. Hence the automatic detection of the clutter and interference is an important step towards extracting them. In this paper, an automatic clutter and interference detection method based on deep learning is proposed to improve the performance of HFSWR. Conventionally, the Range-Doppler (RD) spectrum image processing method requires the target feature extraction including feature design and preselection, which is not only complicated and time-consuming, but the quality of the designed features is bound up with the performance of the algorithm. By analyzing the features of the target, the clutter and the interference in RD spectrum images, a lightweight deep convolutional learning network is established based on a faster region-based convolutional neural networks (Faster R-CNN). By using effective feature extraction combined with a classifier, the clutter and the interference can be automatically detected. Due to the end-to-end architecture and the numerous convolutional features, the deep learning-based method can avoid the difficulty and absence of uniform standard inherent in handcrafted feature design and preselection. Field experimental results show that the Faster R-CNN based method can automatically detect the clutter and interference with decent performance and classify them with high accuracy.
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Zhao, Di, Hongyan Xing, Haifeng Wang, Huaizhou Zhang, Xinyi Liang, and Haoqi Li. "Sea-Surface Small Target Detection Based on Four Features Extracted by FAST Algorithm." Journal of Marine Science and Engineering 11, no. 2 (February 3, 2023): 339. http://dx.doi.org/10.3390/jmse11020339.
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On account of current algorithm and parameter design difficulties and low detection accuracy in feature extractions of small target detections in sea clutter environment, this paper proposes a correspondingly improved four feature extraction method by FAST. After the short-time Fourier transform is applied, a time–frequency distribution spectrogram of original data is generated. Candidate feature points (CFP) are first extracted by FAST algorithm, and then a four-feature extraction is implemented with FAST and DBSCAN combined. The feature distinction is enhanced through a feature optimization. Upon the construction of the four-dimensional feature vectors, XGBoost classifier algorithm classifies and detects these feature vectors. The genetic algorithm optimizes the hyperparameters in XGBoost and updates the decision threshold in real time to control the detection method’s false alarm rate. The IPIX dataset is employed for experimental verification. Verification results confirm that this proposed detection method has better performance than several other currently used detection methods. The detection performance is improved by 7% and 13.8% when observation time is set at 0.512 s and 1.024 s, respectively.
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Duan, Guoxing, Yunhua Wang, Yanmin Zhang, Shuya Wu, and Letian Lv. "A Network Model for Detecting Marine Floating Weak Targets Based on Multimodal Data Fusion of Radar Echoes." Sensors 22, no. 23 (November 25, 2022): 9163. http://dx.doi.org/10.3390/s22239163.
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Due to the interaction between floating weak targets and sea clutter in complex marine environments, it is necessary to distinguish targets and sea clutter from different dimensions by designing universal deep learning models. Therefore, in this paper, we introduce the concept of multimodal data fusion from the field of artificial intelligence (AI) to the marine target detection task. Using deep learning methods, a target detection network model based on the multimodal data fusion of radar echoes is proposed. In the paper, according to the characteristics of different modalities data, the temporal LeNet (T-LeNet) network module and time-frequency feature extraction network module are constructed to extract the time domain features, frequency domain features, and time-frequency features from radar sea surface echo signals. To avoid the impact of redundant features between different modalities data on detection performance, a Self-Attention mechanism is introduced to fuse and optimize the features of different dimensions. The experimental results based on the publicly available IPIX radar and CSIR datasets show that the multimodal data fusion of radar echoes can effectively improve the detection performance of marine floating weak targets. The proposed model has a target detection probability of 0.97 when the false alarm probability is 10−3 under the lower signal-to-clutter ratio (SCR) sea state. Compared with the feature-based detector and the detection model based on single-modality data, the new model proposed by us has stronger detection performance and universality under various marine detection environments. Moreover, the transfer learning method is used to train the new model in this paper, which effectively reduces the model training time. This provides the possibility of applying deep learning methods to real-time target detection at sea.
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Jiang, Yingqi, Lili Dong, and Junke Liang. "Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination." Sensors 22, no. 15 (August 5, 2022): 5873. http://dx.doi.org/10.3390/s22155873.
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Infrared image enhancement technology can effectively improve the image quality and enhance the saliency of the target and is a critical component in the marine target search and tracking system. However, the imaging quality of maritime infrared images is easily affected by weather and sea conditions and has low contrast defects and weak target contour information. At the same time, the target is disturbed by different intensities of sea clutter, so the characteristics of the target are also different, which cannot be processed by a single algorithm. Aiming at these problems, the relationship between the directional texture features of the target and the roughness of the sea surface is deeply analyzed. According to the texture roughness of the waves, the image scene is adaptively divided into calm sea surface and rough sea surface. At the same time, through the Gabor filter at a specific frequency and the gradient-based target feature extraction operator proposed in this paper, the clutter suppression and feature fusion strategies are set, and the target feature image of multi-scale fusion in two types of scenes are obtained, which is used as a guide image for guided filtering. The original image is decomposed into a target and a background layer to extract the target features and avoid image distortion. The blurred background around the target contour is extracted by Gaussian filtering based on the potential target region, and the edge blur caused by the heat conduction of the target is eliminated. Finally, an enhanced image is obtained by fusing the target and background layers with appropriate weights. The experimental results show that, compared with the current image enhancement method, the method proposed in this paper can improve the clarity and contrast of images, enhance the detectability of targets in distress, remove sea surface clutter while retaining the natural environment features in the background, and provide more information for target detection and continuous tracking in maritime search and rescue.
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Pan, Xueli, Nana Li, Lixia Yang, Zhixiang Huang, Jie Chen, Zhenhua Wu, and Guoqing Zheng. "Anomaly-Based Ship Detection Using SP Feature-Space Learning with False-Alarm Control in Sea-Surface SAR Images." Remote Sensing 15, no. 13 (June 24, 2023): 3258. http://dx.doi.org/10.3390/rs15133258.
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Synthetic aperture radar (SAR) can provide high-resolution and large-scale maritime monitoring, which is beneficial to ship detection. However, ship-detection performance is significantly affected by the complexity of environments, such as uneven scattering of ship targets, the existence of speckle noise, ship side lobes, etc. In this paper, we present a novel anomaly-based detection method for ships using feature learning for superpixel (SP) processing cells. First, the multi-feature extraction of the SP cell is carried out, and to improve the discriminating ability for ship targets and clutter, we use the boundary feature described by the Haar-like descriptor, the saliency texture feature described by the non-uniform local binary pattern (LBP), and the intensity attention contrast feature to construct a three-dimensional (3D) feature space. Besides the feature extraction, the target classifier or determination is another key step in ship-detection processing, and therefore, the improved clutter-only feature-learning (COFL) strategy with false-alarm control is designed. In detection performance analyses, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the method’s effectiveness. Many experimental results show that the proposed method can significantly improve the detection performance of ship targets, and has a high detection rate and low false-alarm rate in complex background and multi-target marine environments.
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Farshchian, Masoud. "Target Extraction and Imaging of Maritime Targets in the Sea Clutter Spectrum Using Sparse Separation." IEEE Geoscience and Remote Sensing Letters 14, no. 2 (February 2017): 232–36. http://dx.doi.org/10.1109/lgrs.2016.2636253.
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Ningbo, Liu, Xu Yanan, Ding Hao, Xue Yonghua, and Guan Jian. "High-dimensional feature extraction of sea clutter and target signal for intelligent maritime monitoring network." Computer Communications 147 (November 2019): 76–84. http://dx.doi.org/10.1016/j.comcom.2019.08.016.
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Wu, Zheng Long, Jie Li, and Zhen Yu Guan. "Feature Extraction of Underwater Target Ultrasonic Echo Based on Wavelet Transform." Applied Mechanics and Materials 599-601 (August 2014): 1517–22. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1517.
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Ultrasonic detection has been widely used in underwater detectoscopes as an important method for underwater detection. Feature extraction of echo signal time-delay and amplitude is the main task of processing underwater ultrasonic signal. Underwater target ultrasonic echo signal is influenced by reverberation and noise from the sea and system itself, reverberation interference of signal background is the main difficulty for target echo detection. So we use denoising algorithm to denoise echo signal. At first this paper denoises the measured weighted background clutter data using wavelet threshold denoising method, then the paper extracts breaking points of echo signal through wavelet transform, at last the paper makes an envelope extraction using Hilbert transform combined with wavelet transform methods, and acquires the feature information of echo signal amplitude.
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Chen, Xiaolong, Jian Guan, Zhonghua Bao, and You He. "Detection and Extraction of Target With Micromotion in Spiky Sea Clutter Via Short-Time Fractional Fourier Transform." IEEE Transactions on Geoscience and Remote Sensing 52, no. 2 (February 2014): 1002–18. http://dx.doi.org/10.1109/tgrs.2013.2246574.
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Luo, Zhongtao, Taifeng Wu, Zishu He, and Xuyuan Chen. "Extraction of sea‐clutter and RFI regions based on image segmentation for high‐frequency sky‐wave radar." IET Radar, Sonar & Navigation 13, no. 1 (January 2019): 58–64. http://dx.doi.org/10.1049/iet-rsn.2018.5128.
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Chen, Xiaolong, Jian Guan, Xiaoqian Mu, Zhigao Wang, Ningbo Liu, and Guoqing Wang. "Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet." Remote Sensing 13, no. 19 (September 26, 2021): 3856. http://dx.doi.org/10.3390/rs13193856.
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Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. The prediction frame coordinates, target category, and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. P-NMS can effectively improve the problem of missed detection of multi-targets. Moreover, the false alarms caused by strong sea clutter are reduced by the multi-frame fusion, which is also a benefit for weak target detection. The verification using the X-band navigation radar PPI image dataset shows that compared with the traditional cell-average constant false alarm rate detector (CA-CFAR) and the two-stage Faster R-CNN algorithm, the proposed method significantly improved the detection probability by 15% and 10% under certain false alarm probability conditions, which is more suitable for various environment and target characteristics. Moreover, the computational burden is discussed showing that the Radar-PPInet detection model is significantly lower than the Faster R-CNN in terms of parameters and calculations.
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Hu, Jianming, Xiyang Zhi, Wei Zhang, Longfei Ren, and Lorenzo Bruzzone. "Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images." Remote Sensing 12, no. 20 (October 15, 2020): 3370. http://dx.doi.org/10.3390/rs12203370.
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Automatic ship detection in complicated maritime background is a challenging task in the field of optical remote sensing image interpretation and analysis. In this paper, we propose a novel and reliable ship detection framework based on a visual saliency model, which can efficiently detect multiple targets of different scales in complex scenes with sea clutter, clouds, wake and islands interferences. Firstly, we present a reliable background prior extraction method adaptive for the random locations of targets by computing boundary probability and then generate a saliency map based on the background prior. Secondly, we compute the prior probability of salient foreground regions and propose a weighting function to constrain false foreground clutter, gaining the foreground-based prediction map. Thirdly, we integrate the two prediction maps and improve the details of the integrated map by a guided filter function and a wake adjustment function, obtaining the fine selection of candidate regions. Afterwards, a classification is further performed to reduce false alarms and produce the final ship detection results. Qualitative and quantitative evaluations on two public available datasets demonstrate the robustness and efficiency of the proposed method against four advanced baseline methods.
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Gao, Fei, Wei Shi, Jun Wang, Erfu Yang, and Huiyu Zhou. "Enhanced Feature Extraction for Ship Detection from Multi-Resolution and Multi-Scene Synthetic Aperture Radar (SAR) Images." Remote Sensing 11, no. 22 (November 18, 2019): 2694. http://dx.doi.org/10.3390/rs11222694.
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Independent of daylight and weather conditions, synthetic aperture radar (SAR) images have been widely used for ship monitoring. The traditional methods for SAR ship detection are highly dependent on the statistical models of sea clutter or some predefined thresholds, and generally require a multi-step operation, which results in time-consuming and less robust ship detection. Recently, deep learning algorithms have found wide applications in ship detection from SAR images. However, due to the multi-resolution imaging mode and complex background, it is hard for the network to extract representative SAR target features, which limits the ship detection performance. In order to enhance the feature extraction ability of the network, three improvement techniques have been developed. Firstly, multi-level sparse optimization of SAR image is carried out to handle clutters and sidelobes so as to enhance the discrimination of the features of SAR images. Secondly, we hereby propose a novel split convolution block (SCB) to enhance the feature representation of small targets, which divides the SAR images into smaller sub-images as the input of the network. Finally, a spatial attention block (SAB) is embedded in the feature pyramid network (FPN) to reduce the loss of spatial information, during the dimensionality reduction process. In this paper, experiments on the multi-resolution SAR images of GaoFen-3 and Sentinel-1 under complex backgrounds are carried out and the results verify the effectiveness of SCB and SAB. The comparison results also show that the proposed method is superior to several state-of-the-art object detection algorithms.
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Li, B., B. Xu, and Y. Yuan. "Extraction of mixed-order multicomponent ship target signals from broadened sea clutter in bistatic shipborne surface wave radar." IET Radar, Sonar & Navigation 3, no. 3 (2009): 214. http://dx.doi.org/10.1049/iet-rsn:20070138.
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Joshi, Sushil Kumar, Stefan V. Baumgartner, Andre B. C. da Silva, and Gerhard Krieger. "Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection." Remote Sensing 11, no. 11 (May 28, 2019): 1270. http://dx.doi.org/10.3390/rs11111270.
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Ship detection is an essential maritime security requirement. Current state-of-the-art synthetic aperture radar (SAR) based ship detection methods employ fully focused images. The time-consuming processing efforts required to generate these images make them generally unsuitable for real time applications. This paper proposes a novel real time oriented ship detection strategy applicable to range-compressed (RC) radar data acquired by an airborne radar sensor during linear, circular and arbitrary flight tracks. A constant false alarm rate (CFAR) detection threshold is computed in the range-Doppler domain using suitable distribution functions. Detection in range-Doppler has the advantage that principally even small ships with a low radar cross section (RCS) can be detected if they are moving fast enough so that the ship signals are shifted to the exo-clutter region. In order to determine a robust threshold, the ocean statistics have to be described accurately. Bright target peaks in the background ocean data bias the statistics and lead to an erroneous threshold. Therefore, an automatic ocean training data extraction procedure is proposed in the paper. It includes (1) a novel target pre-detection module that removes the bright peaks from the data already in time domain, (2) clutter normalization in the Doppler domain using the remaining samples, (3) ocean statistics estimation and (4) threshold computation. Various sea clutter models are investigated and analyzed in the paper for finding the most suitable models for the RC data. The robustness and applicability of the proposed method is validated using real linearly and circularly acquired radar data from DLR’s (Deutsches Zentrum für Luft- und Raumfahrt) airborne F-SAR system.
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Kubicek, Bernice, Ananya Sen Gupta, and Ivars Kirsteins. "Statistical-based feature extraction and classification of active sonar data." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A267—A268. http://dx.doi.org/10.1121/10.0011297.
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Sonar target recognition is difficult due to the potential nonlinear overlap within an acoustic color response due to various backscatter and clutter within the ocean. This talk presents initial results from using a statistical model of feature vectors in conjunction with machine learning classifiers. Canonical correlation analysis (CCA) seeks to find two linear combinations of data by maximizing the correlation between the linear combinations while maintaining unit variance. In this application, CCA is used as a feature extraction method before target classification of active sonar data experimentally collected during the Shallow Water Active Classification (SWAC)-1 and SWAC-2 sea trials in the Malta Channel. The database consists of 20 targets; three were analyzed using this method. The data are generated by taking windows of consecutive pings from the ping-vs-time domain and performing CCA. The intuition behind using CCA is that there are persistent features within the data that morph over time due to changing target aspect angles and platform positions which can be represented by the maximally correlated linear combinations of data among consecutive pings. The resulting linear combinations are feature vectors used to train a single hidden-layer neural network classifier. Results are reported as overall classification accuracy and confusion matrices.
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Lee, Seungwoo, Iksu Seo, Jongwon Seok, Yunsu Kim, and Dong Seog Han. "Active Sonar Target Classification with Power-Normalized Cepstral Coefficients and Convolutional Neural Network." Applied Sciences 10, no. 23 (November 26, 2020): 8450. http://dx.doi.org/10.3390/app10238450.
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Detection and classification of unidentified underwater targets maneuvering in complex underwater environments are critical for active sonar systems. In previous studies, many detection methods were applied to separate targets from the clutter using signals that exceed a preset threshold determined by the sonar console operator. This is because the high signal-to-noise ratio target has enough feature vector components to separate. However, in a real environment, the signal-to-noise ratio of the received target does not always exceed the threshold. Therefore, a target detection algorithm for various target signal-to-noise ratio environments is required; strong clutter energy can lead to false detection, while weak target signals reduce the probability of detection. It also uses long pulse repetition intervals for long-range detection and high ambient noise, requiring classification processing for each ping without accumulating pings. In this study, a target classification algorithm is proposed that can be applied to signals in real underwater environments above the noise level without a threshold set by the sonar console operator, and the classification performance of the algorithm is verified. The active sonar for long-range target detection has low-resolution data; thus, feature vector extraction algorithms are required. Feature vectors are extracted from the experimental data using Power-Normalized Cepstral Coefficients for target classification. Feature vectors are also extracted with Mel-Frequency Cepstral Coefficients and compared with the proposed algorithm. A convolutional neural network was employed as the classifier. In addition, the proposed algorithm is to be compared with the result of target classification using a spectrogram and convolutional neural network. Experimental data were obtained using a hull-mounted active sonar system operating on a Korean naval ship in the East Sea of South Korea and a real maneuvering underwater target. From the experimental data with 29 pings, we extracted 361 target and 3351 clutter data. It is difficult to collect real underwater target data from the real sea environment. Therefore, the number of target data was increased using the data augmentation technique. Eighty percent of the data was used for training and the rest was used for testing. Accuracy value curves and classification rate tables are presented for performance analysis and discussion. Results showed that the proposed algorithm has a higher classification rate than Mel-Frequency Cepstral Coefficients without affecting the target classification by the signal level. Additionally, the obtained results showed that target classification is possible within one ping data without any ping accumulation.
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Pang, Lei, Baoxuan Li, Fengli Zhang, Xichen Meng, and Lu Zhang. "A Lightweight YOLOv5-MNE Algorithm for SAR Ship Detection." Sensors 22, no. 18 (September 19, 2022): 7088. http://dx.doi.org/10.3390/s22187088.
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Unlike optical satellites, synthetic aperture radar (SAR) satellites can operate all day and in all weather conditions, so they have a broad range of applications in the field of ocean monitoring. The ship targets’ contour information from SAR images is often unclear, and the background is complicated due to the influence of sea clutter and proximity to land, leading to the accuracy problem of ship monitoring. Compared with traditional methods, deep learning has powerful data processing ability and feature extraction ability, but its complex model and calculations lead to a certain degree of difficulty. To solve this problem, we propose a lightweight YOLOV5-MNE, which significantly improves the training speed and reduces the running memory and number of model parameters and maintains a certain accuracy on a lager dataset. By redesigning the MNEBlock module and using CBR standard convolution to reduce computation, we integrated the CA (coordinate attention) mechanism to ensure better detection performance. We achieved 94.7% precision, a 2.2 M model size, and a 0.91 M parameter quantity on the SSDD dataset.
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Hu, Jianming, Xiyang Zhi, Tianjun Shi, Lijian Yu, and Wei Zhang. "Ship Detection via Dilated Rate Search and Attention-Guided Feature Representation." Remote Sensing 13, no. 23 (November 29, 2021): 4840. http://dx.doi.org/10.3390/rs13234840.
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Due to the complexity of scene interference and the variability of ship scale and position, automatic ship detection in remote sensing images makes for challenging research. The existing deep networks rarely design receptive fields that fit the target scale based on training data. Moreover, most of them ignore the effective retention of position information in the feature extraction process, which reduces the contribution of features to subsequent classification. To overcome these limitations, we propose a novel ship detection framework combining the dilated rate selection and attention-guided feature representation strategies, which can efficiently detect ships of different scales under the interference of complex environments such as clouds, sea clutter and mist. Specifically, we present a dilated convolution parameter search strategy to adaptively select the dilated rate for the multi-branch extraction architecture, adaptively obtaining context information of different receptive fields without sacrificing the image resolution. Moreover, to enhance the spatial position information of the feature maps, we calculate the correlation of spatial points from the vertical and horizontal directions and embed it into the channel compression coding process, thus generating the multi-dimensional feature descriptors which are sensitive to direction and position characteristics of ships. Experimental results on the Airbus dataset demonstrate that the proposed method achieves state-of-the-art performance compared with other detection models.
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Li, Guannan, Ying Li, Yongchao Hou, Xiang Wang, and Lin Wang. "Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data." Remote Sensing 13, no. 9 (April 21, 2021): 1607. http://dx.doi.org/10.3390/rs13091607.
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Marine oil spill detection is vital for strengthening the emergency commands of oil spill accidents and repairing the marine environment after a disaster. Polarimetric Synthetic Aperture Radar (Pol-SAR) can obtain abundant information of the targets by measuring their complex scattering matrices, which is conducive to analyze and interpret the scattering mechanism of oil slicks, look-alikes, and seawater and realize the extraction and detection of oil slicks. The polarimetric features of quad-pol SAR have now been extended to oil spill detection. Inspired by this advancement, we proposed a set of improved polarimetric feature combination based on polarimetric scattering entropy H and the improved anisotropy A12–H_A12. The objective of this study was to improve the distinguishability between oil slicks, look-alikes, and background seawater. First, the oil spill detection capability of the H_A12 combination was observed to be superior than that obtained using the traditional H_A combination; therefore, it can be adopted as an alternate oil spill detection strategy to the latter. Second, H(1 − A12) combination can enhance the scattering randomness of the oil spill target, which outperformed the remaining types of polarimetric feature parameters in different oil spill scenarios, including in respect to the relative thickness information of oil slicks, oil slicks and look-alikes, and different types of oil slicks. The evaluations and comparisons showed that the proposed polarimetric features can indicate the oil slick information and effectively suppress the sea clutter and look-alike information.
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Yang, Xuguang, Changjun Yu, Aijun Liu, Linwei Wang, and Taifan Quan. "The Vertical Ionosphere Parameters Inversion for High Frequency Surface Wave Radar." International Journal of Antennas and Propagation 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/8609372.
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High Frequency Surface Wave Radar (HFSWR), which is currently applied in over-the-horizon detection of targets and sea states remote sensing, can receive a huge mass of ionospheric echoes, making it possible for the ionospheric clutter suppression to become a hot spot in research area. In this paper, from another perspective, we take the ionospheric echoes as the signal source rather than clutters, which provides the possibility of extracting information regarding the ionosphere region and explores a new application field for HFSWR. Primarily, pretreatment of threshold segmentation as well as connected region generation is used in the Range-Doppler (R-D) Spectrum to extract the ionospheric echoes. Then, electron density and plasma frequency of field aligned irregularities (FAIs) caused by plasma instabilities in the F region are obtained by the coherent backscattered radar equation. The plasma drift velocity of FAIs can also be estimated from Doppler shift. Ultimately, the effectiveness of inversion is verified by comparing with IRI2012.
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Huo, Weibo, Jifang Pei, Yulin Huang, Qian Zhang, and Jianyu Yang. "A New Maritime Moving Target Detection and Tracking Method for Airborne Forward-looking Scanning Radar." Sensors 19, no. 7 (April 2, 2019): 1586. http://dx.doi.org/10.3390/s19071586.
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Maritime moving target detection and tracking through particle filter based track-before-detect (PF-TBD) has significant practical value for airborne forward-looking scanning radar. However, villainous weather and surging of ocean waves make it extremely difficult to accurately obtain a statistical model for sea clutter. As the likelihood ratio calculation in PF-TBD is dependent on the distribution of the clutter, the performance of traditional distribution-based PF-TBD seriously declines. To resolve these difficulties, this paper proposes a new target detection and tracking method, named spectral-residual-binary-entropy-based PF-TBD (SRBE-PF-TBD), which is independent from the prior knowledge of sea clutter. In the proposed method, the likelihood ratio calculation is implemented by first extracting the spectral residual of the input image to obtain the saliency map, and then constructing likelihood ratio through a binarization processing and information entropy calculation. Simulation results show that the proposed method had superior performance of maritime moving target detection and tracking.
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Yang, Tian-Ci, Ye Zhao, Guo-Shan Wu, and Xin-Cheng Ren. "Study on Doppler Spectra of Electromagnetic Scattering of Time-Varying Kelvin Wake on Sea Surface." Sensors 22, no. 19 (October 6, 2022): 7564. http://dx.doi.org/10.3390/s22197564.
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In general, it is more practical to detect ship wake under the background of a complicated sea state than the ship directly. Thus, in this paper, the Doppler spectra of time-varying Kelvin wake on a time-varying sea surface are numerically investigated by considering the change of ship wake with time in ocean environments. For this purpose, the linear superposition model of a time-varying sea surface and a time-varying Kelvin wake is established. Combined with the facet scattering field model of sea surface and Kirchhoff approximation (KA), the Doppler of the radar scattering echo signal of the time-varying wake on the sea surface is simulated and analyzed under different polarizations, incident angles, ship speeds, and wind speeds, as well as wind directions. It can be observed that the Doppler spectrum changes as the conditions change. This study provides a reference for extracting motion features of ship wakes in a sea clutter environment.
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Yang, Zhiqing, Jianjiang Tang, Hao Zhou, Xinjun Xu, Yingwei Tian, and Biyang Wen. "Joint Ship Detection Based on Time-Frequency Domain and CFAR Methods with HF Radar." Remote Sensing 13, no. 8 (April 16, 2021): 1548. http://dx.doi.org/10.3390/rs13081548.
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Compact high-frequency surface wave radar (HFSWR) plays a critical role in ship surveillance. Due to the wide antenna beam-width and low spatial gain, traditional constant false alarm rate (CFAR) detectors often induce a low detection probability. To solve this problem, a joint detection algorithm based on time-frequency (TF) analysis and the CFAR method is proposed in this paper. After the TF ridge extraction, CFAR detection is performed to test each sample of the ridges, and a binary integration is run to determine whether the entire TF ridge is of a ship. To verify the effectiveness of the proposed algorithm, experimental data collected by the Ocean State Monitoring and Analyzing Radar, type SD (OSMAR-SD) were used, with the ship records from an automatic identification system (AIS) used as ground truth data. The processing results showed that the joint TF-CFAR method outperformed CFAR in detecting non-stationary and weak signals and those within the first-order sea clutters, whereas CFAR outperformed TF-CFAR in identifying multiple signals with similar frequencies. Notably, the intersection of the matched detection sets by TF-CFAR and CFAR alone was not immense, which takes up approximately 68% of the matched number by CFAR and 25% of that by TF-CFAR; however, the number in the union detection sets was much (>30%) greater than the result of either method. Therefore, joint detection with TF-CFAR and CFAR can further increase the detection probability and greatly improve detection performance under complicated situations, such as non-stationarity, low signal-to-noise ratio (SNR), and within the first-order sea clutters.
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Sun, Lei, Zhizhong Lu, Hui Wang, Hong Liu, and Xiuneng Shang. "A Wave Texture Difference Method for Rainfall Detection Using X-Band Marine Radar." Journal of Sensors 2022 (February 18, 2022): 1–16. http://dx.doi.org/10.1155/2022/1068885.
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To suppress the influence of rainfall when extracting sea surface wind and wave parameters using X-band marine radar and control the quality of the collected radar image, it is necessary to detect whether the radar image is contaminated by rainfall. Since the detection accuracy of the statistical characteristics methods (e.g., the zero pixel percentage method and the high-clutter direction method) is limited and the threshold is difficult to determine, the machine learning methods (e.g., the support vector machine-based method and the neural network algorithm) are difficult to select appropriate quality and quantity of data for model training. Therefore, based on the feature that rainfall can change the sea surface texture, a wave texture difference method for rainfall detection is proposed in this paper. Considering the spatial rainfall is uneven, the polar coordinates of the radar image are converted into Cartesian coordinates to detect rainfall. To express the maximum wave difference more accurately, the calculation method of the pixels in the radar texture difference map is redefined. Then, a consecutive pixel method is used to detect the calculated radar texture difference map, and this method can detect adaptively with the change of wind. The data collected from the shore of Haitan Island along the East China Sea are used to validate the effectiveness of the proposed method. Compared with the zero pixel percentage method and the support vector machine-based method, the experimental results demonstrate that the proposed method has better rainfall detection performance. In addition, the research on the applicability of the proposed method shows that the wave texture difference method can finish the task of rainfall detection in most marine environments.
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Su, Liyun, and Xiu Ling. "Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network." Complexity 2020 (July 20, 2020): 1–14. http://dx.doi.org/10.1155/2020/3284587.
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In target estimating sea clutter or actual mechanical fault diagnosis, useful signal is often submerged in strong chaotic noise, and the targeted signal data are difficult to recover. Traditional schemes, such as Elman neural network (ENN), backpropagation neural network (BPNN), support vector machine (SVM), and multilayer perceptron- (MLP-) based model, are insufficient to extract the weak signal embedded in a chaotic background. To improve the estimating accuracy, a novel estimating method for aiming at extracting problem of weak pulse signal buried in a strong chaotic background is presented. Firstly, the proposed method obtains the vector sequence signal by reconstructing higher-dimensional phase space data matrix according to the Takens theorem. Then, a Jordan neural network- (JNN-) based model is designed, which can minimize the error squared sum by mixing the single-point jump model for targeting signal. Finally, based on short-term predictability of chaotic background, estimation of weak pulse signal from the chaotic background is achieved by a profile least square method for optimizing the proposed model parameters. The data generated by the Lorenz system are used as chaotic background noise for the simulation experiment. The simulation results show that Jordan neural network and profile least square algorithm are effective in estimating weak pulse signal from chaotic background. Compared with the traditional method, (1) the presented method can estimate the weak pulse signal in strong chaotic noise under lower error than ENN-based, BPNN-based, SVM-based, and -ased models and (2) the proposed method can extract the weak pulse signal under a higher output SNR than BPNN-based model.
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Chen, Zhe, Zhiquan Ding, Xiaoling Zhang, Xiaoting Wang, and Yuanyuan Zhou. "Inshore Ship Detection Based on Multi-Modality Saliency for Synthetic Aperture Radar Images." Remote Sensing 15, no. 15 (August 4, 2023): 3868. http://dx.doi.org/10.3390/rs15153868.
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Synthetic aperture radar (SAR) ship detection is of significant importance in military and commercial applications. However, a high similarity in intensity and spatial distribution of scattering characteristics between the ship target and harbor facilities, along with a fuzzy sea-land boundary due to the strong speckle noise, result in a low detection accuracy and high false alarm rate for SAR ship detection with complex inshore scenes. In this paper, a new inshore ship detection method based on multi-modality saliency is proposed to overcome these challenges. Four saliency maps are established from different perspectives: an ocean-buffer saliency map (OBSM) outlining more accurate coastline under speckle noises; a local stability saliency map (LSSM) addressing pixel spatial distribution; a super-pixel saliency map (SPSM) extracting critical region-based features for inshore ship detection; and an intensity saliency map (ISM) to highlight target pixels with intensity distribution. By combining these saliency maps, ship targets in complex inshore scenes can be successfully detected. The method provides a novel interdisciplinary perspective (surface metrology) for SAR image segmentation, discovers the difference in spatial characteristics of SAR image elements, and proposes a novel robust CFAR procedure for background clutter fitting. Experiments on a public SAR ship detection dataset (SSDD) shows that our method achieves excellent detection performance, with a low false alarm rate, in offshore scenes, inshore scenes, inshore scenes with confusing metallic port facilities, and large-scale scenes. The results outperform several widely used methods, such as CFAR-based methods and super-pixel methods.
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Shao, Zhiyu, Jiangheng He, and Shunshan Feng. "Extraction of a target in sea clutter via signal decomposition." Science China Information Sciences 63, no. 2 (September 24, 2019). http://dx.doi.org/10.1007/s11432-018-9859-4.
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Bi, Xiaowen, Shenglong Guo, Yunxiu Yang, and Qin Shu. "Adaptive Target Extraction Method in Sea Clutter Based on Fractional Fourier Filtering." IEEE Transactions on Geoscience and Remote Sensing, 2022, 1. http://dx.doi.org/10.1109/tgrs.2022.3192893.
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Wu, Xijie, Hao Ding, Ningbo Liu, Yunlong Dong, and Jian Guan. "Priori Information Based Feature Extraction Method for Small Target Detection in Sea Clutter." IEEE Transactions on Geoscience and Remote Sensing, 2022, 1. http://dx.doi.org/10.1109/tgrs.2022.3188046.
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Li, Qing. "Spatio-temporal nonconvex penalty adaptive chirp mode decomposition for signal decomposition of cross-frequency coupled sources in seafloor dynamic engineering." Frontiers in Marine Science 9 (October 19, 2022). http://dx.doi.org/10.3389/fmars.2022.1008242.
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Electromagnetic field noise and clutter generated from the motion of ocean waves are the main obstacles in the research of magnetotelluric dynamic analysis, and it is difficult to extract the crossed instantaneous frequencies (IFs) of underwater electromagnetic detected (UEMD) data due to the limited resolution of the current time-frequency techniques. To alleviate this bottleneck issue, a new spatio-temporal nonconvex penalty adaptive chirp mode decomposition (STNP-ACMD) is originally proposed for separating each mono-component individually from a complicated multi-component with severely crossed IFs or overlapped components, in this paper. Specifically, the idea of a nonconvex penalty greedy strategy is incorporated into the vanilla ACMD method by using a recursive mode extraction scheme, and the fractional-order characteristic of the observation signal is also considered. Meanwhile, the spatio-temporal matrices were constructed elaborately and then applied to capture coupling characteristics and spatio-temporal relationships among all estimated mono-components. Eventually, a high-resolution adaptive time-frequency spectrum is obtained according to the IFs and instantaneous amplitudes (IAs) of each estimated mono-component. The effectiveness and practicability of the proposed algorithm were verified via simulated scenarios and velocity dynamic data of the seafloor from the South China Sea, compared with four state-of-the-art benchmarks.