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.

中文翻译：

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基于改进GWO算法的RBF神经网络模型的HFSWR海杂波抑制方法

高频表面波雷达（HFSWR）的检测性能与海杂波的抑制效果密切相关。为了有效抑制海浪杂波，提出了一种基于径向基函数神经网络（RBFNN）的海浪杂波抑制方法。首先，针对标准灰狼优化算法的收敛速度慢，容易陷入局部最优等缺点，提出了一种自适应的分工搜索策略，使种群既具有大规模搜索的能力和整个优化过程中的局部探索。然后，使用IGWO算法对RBFNN进行优化，最后建立海杂波预测模型（IGWO-RBFNN），实现海杂波的预测和抑制。实验表明，IGWO算法具有明显的收敛速度和优化精度。与线性降权策略的粒子群算法和GWO算法相比，IGWO算法优化的RBFNN预测模型具有更高的预测精度，对HFSWR的海杂波具有更好的抑制效果。