基于改进PSO-WNN的高频地波雷达 电离层杂波抑制方法

高频地波雷达的海上目标探测能力与电离层杂波的抑制效果息息相关，而电离层杂波的复杂性与变化多样性又为抑制带来了难题。为实现电离层杂波的抑制，分析了电离层杂波的混沌特性，在此基础上提出一种基于改进粒子群算法优化小波神经网络的抑制方法，解决了粒子群算法易早熟和易陷入局部最优的缺点；提出一种自适应概率变异的策略，丰富了种群多样性，使得整个迭代过程中粒子群能够跳出当前最优，寻得全局最优。实测实验表明，基于改进粒子群算法优化的小波神经网络（PSO-WNN）能够基本预测电离层杂波的数值，进行电离层杂波的抑制，有效改善了信噪比，对电离层杂波的抑制研究具有重要意义。

Ionospheric clutter suppression method for HF ground wave radar based on improved PSO-WNN

The sea target detection ability of HF ground wave radar is closely related to the suppression effect of ionospheric clutter, and the complexity and diversity of ionospheric clutter bring difficulties to the suppression. In order to suppress ionospheric clutter, the chaotic characteristics of ionospheric clutter are analyzed. On this basis, a suppression method based on improved particle swarm optimization to optimize wavelet neural network is proposed, which solves the shortcomings of particle swarm optimization that it is easy to premature and fall into local optimization. An adaptive probability mutation strategy is proposed to enrich the population diversity, the particle swarm optimization can jump out of the current optimal and find the global optimal in the whole iterative process. The experimental results show that the wavelet neural network optimized based on the improved particle swarm optimization algorithm can basically predict the value of ionospheric clutter. After the suppression of ionospheric clutter, the signal-to-noise ratio is effectively improved, and the research on the suppression of ionospheric clutter is of great significance.