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

Abstract

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.

Prediction and Estimation of Book Borrowing in the Library: Machine learning

“…This study mainly analyzed RBFNN. For the parameter selection of RBFNN, many algorithms have been applied, such as the gravity search algorithm [21], genetic algorithm [22], grey wolf optimization algorithm [23], etc. This paper selected the ACO algorithm, improved the ACO algorithm to optimize the parameters of RBFNN, carried out experiments with the actual book lending data, and compared the IACO-RBFNN model with the BPNN and RBFNN models.…”

Deep learning method for remaining useful life prediction of the rolling bearing of high-speed train

“…With the mature application of deep learning methods and the emergence of optimization algorithms, more complex deep cyclic networks continue to show their advantages and make important achievements in time series prediction.Based on the particle swarm optimization algorithm, the back propagation neural network (BPNN) has been optimized, and has obtained better robustness and prediction accuracy [11]. Based on the improved gray wolf optimization, the radial basis function neural network (RBFNN) has been optimized [12]. However, due to the structure of the BPNN and RBFNN, it may lead to gradient vanishing and gradient explosion, which are inapplicable for the prediction of time series information with long time span.…”

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

“…At present, target detection methods under sea clutter suppression are mainly divided into four categories in [ 12 , 13 , 14 ]: time-domain cancellation method, subspace decomposition method, neural network clutter suppression method and time-frequency analysis. Time-domain cancellation method mainly includes moving target indication (MTI) [ 15 ], motion target detection (MTD) [ 16 , 17 , 18 , 19 , 20 ], adaptive moving target indication (AMTI) [ 21 , 22 ], space-time adaptive processing (STAP) [ 23 , 24 , 25 , 26 ] and root loop cancellation method [ 27 , 28 ].…”

“…Neural network clutter suppression method includes convolutional neural network [ 14 , 39 , 40 , 41 ], radial basis function neural network [ 13 , 42 , 43 ], wavelet neural network [ 44 , 45 ], etc. The neural network clutter suppression method is to train and optimize itself by using the chaotic characteristics and predictability of sea clutter and, thus, to establish the prediction model of sea clutter.…”