A convolutional neural network based approach to sea clutter suppression for small boat detection

2020 ◽  
Vol 21 (10) ◽  
pp. 1504-1520
Author(s):  
Guan-qing Li ◽  
Zhi-yong Song ◽  
Qiang Fu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
Small Boat

A Novel Sea Clutter Suppression Method based on Neural Network

2021 ◽  
Author(s):  
Yifei Fan ◽  
Chenxiang Li ◽  
Dongtao Li ◽  
Jieshuang Li ◽  
Jia Su ◽  
...  
Keyword(s):  
Neural Network ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
Suppression Method

scholarly journals Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2826 ◽  
Author(s):  
Ma ◽  
Wu ◽  
Zhang ◽  
Wu ◽  
Jeon ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Good Effect ◽  
Prediction System ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Term Variation ◽  
Long Short Term Memory

In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.

scholarly journals Sea Clutter Suppression Method of HFSWR Based on RBF Neural Network Model Optimized by Improved GWO Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shang Shang ◽  
Kang-Ning He ◽  
Zhao-Bin Wang ◽  
Tong Yang ◽  
Ming Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convergence Speed ◽  
Particle Swarm Algorithm ◽  
Gray Wolf ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
Suppression Effect ◽  
Wave Radar ◽  
Suppression Method

The detection performance of high-frequency surface-wave radar (HFSWR) is closely related to the suppression effect of sea clutter. To effectively suppress sea clutter, a sea clutter suppression method based on radial basis function neural network (RBFNN) optimized by improved gray wolf optimization (IGWO) algorithm is proposed. Firstly, according to shortcomings of the standard gray wolf optimization (GWO) algorithm, such as slow convergence speed and easily getting into local optimum, an adaptive division of labor search strategy is proposed, which makes the population have abilities of both large-scale search and local exploration in the entire optimization process. Then, the IGWO algorithm is used to optimize RBFNN, finally, establishing a sea clutter prediction model (IGWO-RBFNN) and realizing the prediction and suppression of sea clutter. Experiments show that the IGWO algorithm has significantly improved convergence speed and optimization accuracy. Compared with the particle swarm algorithm with linear decreasing weight strategy (LDWPSO) and the GWO algorithm, the RBFNN prediction model optimized by the IGWO algorithm has higher prediction accuracy and has a better suppression effect on sea clutter of HFSWR.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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