Sea Clutter Suppression for Radar PPI Images Based on SCS-GAN

Mou, Xiaoqian; Chen, Xiaolong; Guan, Jian; Dong, Yunlong; Liu, Ningbo

doi:10.1109/lgrs.2020.3012523

Cited by 27 publications

(12 citation statements)

References 10 publications

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“…Although the measured radar sea clutter data can more truly reflect the marine environment detected by radar, the cost of recording the measured sea clutter data is often high, and it is difficult to obtain sea clutter under different observation conditions. Therefore, in practical applications, computer simulation technology is first used to generate a large number of sea clutter data with different characteristics, and then to verify the performance of radar in the sea clutter environment, especially for the radar target detection algorithm [5][6] in the sea clutter background performance verification. Sea clutter simulation technology mainly includes two aspects, one is the mathematical modeling of sea clutter statistical characteristics; the other is the generation of sea clutter data.…”

Section: Introductionmentioning

confidence: 99%

Simulation of spatially-temporally correlated radar sea clutter with generalized inverse Gaussian texture

xue,

zhu,

sun

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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A comprehensive assessment of the performance of maritime radar needs to rely on sea clutter data with different characteristics. However, the limitations of radar field tests lead to the fact that measured sea clutter data with different characteristics are often difficult to obtain. In practice, the problem of limited sea clutter data can be effectively solved by computer simulation. For the sea clutter simulation problem, this paper investigates the sea clutter simulation method with spatial correlation, temporal correlation and variable non-Gaussianity. Firstly, through the statistical analysis of the measured sea clutter data, it is concluded that the temporal correlation of sea clutter in a short time is determined by its speckle component, the spatial correlation is determined by its texture component, and the non-Gaussian property is determined by the probability distribution of the texture component. Based on the aforementioned sea clutter properties and the theory of spherically invariant stochastic processes, a sea clutter simulation method with generalized inverse Gaussian texture and spatial-temporal correlation is proposed. Finally, the proposed simulation method is evaluated experimentally, and the experimental results show that the proposed sea clutter simulation method can effectively simulate sea clutter with generalized inverse Gaussian texture and spatial-temporal correlation.

show abstract

Section: Introductionmentioning

confidence: 99%

Simulation of spatially-temporally correlated radar sea clutter with generalized inverse Gaussian texture

xue,

zhu,

sun

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

View full text Add to dashboard Cite

show abstract

“…In the training phase of these methods, a large number of clutter sample and corresponding clutter-free sample pairs are often needed as training samples. In order to effectively utilize training samples, [29] proposed an unsupervised sea clutter suppression method based on generative adversarial networks (GANs) and applied in the target detection of marine radar plan position indicator images.…”

Section: Introductionmentioning

confidence: 99%

A Sea Clutter Suppression Method Based on Machine Learning Approach for Marine Surveillance Radar

Pei

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Because it was the study of artificial neural networks, it was also called deep neural networks (DNNs). Later, it gradually developed into an important branch of machine learning, including the convolutional neural network (CNN) [20], recurrent neural network (RNN) [21], deep belief network (DBN) [22], generative adversarial network (GAN) [23,24], etc. It can automatically learn and extract the features of the signal or image, and realize tasks, such as intelligent recognition of speech information, detection, and segmentation of images.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there is an urgent need to design a radar image target detection method suitable for a complex sea clutter background. Recently, we carried out studies on radar image detection based on deep learning, and proposed classification of marine targets with micro-motion based on CNN [32], integrated network (INet) for clutter suppression and target detection [33], sea clutter suppression generative adversarial network (SCSGAN) [24] etc., using the radar range, azimuth, and time-frequency information to improve the detection performance. In this paper, a novel Radar-PPInet is designed for PPI image detection in complex scenarios (ocean, land, islands, etc.).…”

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet

Chen

Guan

et al. 2021

Remote Sensing

Self Cite

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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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Sea Clutter Suppression for Radar PPI Images Based on SCS-GAN

Cited by 27 publications

References 10 publications

Simulation of spatially-temporally correlated radar sea clutter with generalized inverse Gaussian texture

Simulation of spatially-temporally correlated radar sea clutter with generalized inverse Gaussian texture

A Sea Clutter Suppression Method Based on Machine Learning Approach for Marine Surveillance Radar

Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet

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