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.

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

Sea clutter suppression method for shipborne high‐frequency surface‐wave radar

2016 ◽  
Vol 10 (1) ◽  
pp. 107-113 ◽  
Author(s):  
Chunlei Yi ◽  
Zhenyuan Ji ◽  
Junhao Xie ◽  
Minglei Sun ◽  
Yang Li
Keyword(s):  
Surface Wave ◽  
High Frequency ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
Wave Radar ◽  
Suppression Method

scholarly journals A Single-Dataset-Based Pre-Processing Joint Domain Localized Algorithm for Clutter-Suppression in Shipborne High-Frequency Surface-Wave Radar

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3773
Author(s):  
Liang Guo ◽  
Xin Zhang ◽  
Di Yao ◽  
Qiang Yang ◽  
Yang Bai ◽  
...  
Keyword(s):  
Surface Wave ◽  
High Frequency ◽  
Secondary Data ◽  
Real Data ◽  
Training Sample ◽  
Clutter Suppression ◽  
Low Velocity ◽  
Unscented Transformation ◽  
Wave Radar ◽  
Suppression Method

Due to the motion of the platform, the spectrum of first-order sea clutter will widen and mask low-velocity targets such as ships in shipborne high-frequency surface-wave radar (HFSWR). Limited by the quantity of qualified training samples, the performance of the generally used clutter-suppression method, space–time adaptive processing (STAP) degrades in shipborne HFSWR. To deal with this problem, an innovative training sample acquisition method is proposed, in the area of joint domain localized (JDL) reduced-rank STAP. In this clutter-suppression method, based on a single range of cell data, the unscented transformation is introduced as a preprocessing step to obtain adequate homogeneous secondary data and roughly estimated clutter covariance matrix (CCM). The accurate CCM is calculated by integrating the approximate CCM of different range of cells. Compared with existing clutter-suppression algorithms for shipborne HFSWR, the proposed approach has a better signal-to-clutter-plus-noise ratio (SCNR) improvement tested by real data.

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 A Knowledge-Based Auxiliary Channel STAP for Target Detection in Shipborne HFSWR

2021 ◽  
Vol 13 (4) ◽  
pp. 621
Author(s):  
Liang Guo ◽  
Weibo Deng ◽  
Di Yao ◽  
Qiang Yang ◽  
Lei Ye ◽  
...  
Keyword(s):  
Space Time ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
First Order ◽  
Knowledge Based ◽  
Training Samples ◽  
Wave Radar ◽  
Samples Selection ◽  
Space Time Adaptive Processing ◽  
Sidelobe Canceller

The broadened first-order sea clutter in shipborne high frequency surface wave radar (HFSWR), which will mask the targets with low radial velocity, is a kind of classical space–time coupled clutter. Space–time adaptive processing (STAP) has been proven to be an effective clutter suppression algorithm for space-time coupled clutter. To further improve the efficiency of clutter suppression, a STAP method based on a generalized sidelobe canceller (GSC) structure, named as the auxiliary channel STAP, was introduced into shipborne HFSWR. To obtain precise clutter information for the clutter covariance matrix (CCM) estimation, an approach based on the prior knowledge to auxiliary channel selection is proposed. Auxiliary channels are selected along the clutter ridge of the first-order sea clutter, whose distribution can be determined by the system parameters and regarded as pre-knowledge. To deal with the heterogeneity of the spreading first-order sea clutter, an innovative training samples selection approach according to the Riemannian distance is presented. The range cells that had shorter Riemannian distances to the cell under test (CUT) were chosen as training samples. Experimental results with measured data verified the effectiveness of the proposed algorithm, and the comparison with the existing clutter suppression algorithms showed the superiority of the algorithm.

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