基于OARO-GRU网络的高频地波雷达电离层杂波短期预测

电离层杂波的精确预测对提升高频地波雷达的目标探测性能具有重要推动作用。为此，提出了一种基于改进人工兔子算法优化门控循环单元 （Opposite Artificial Rabbits Optimization optimized Gated Recurrent Unit， OARO-GRU）网络的电离层杂波短期预测模型。首先，依据高频地波雷达接收到的电离层杂波具有混沌特性这一先验知识，通过相空间重构技术构造GRU网络的输入和输出样本集；然后，融入反向学习和柯西变异两种改进策略用于改善标准ARO的寻优能力，并将其用于执行GRU网络的包含隐层节点个数、初始学习速率和最大迭代次数在内的三个超参数值的优选；最后，重新训练优化后的GRU网络，输入测试样本集进行测试，并依据给定的评价指标评估模型。实测结果表明：相较于其他7种对照模型，所提出的OARO-GRU网络预测模型在预测精度和可靠性上均具有明显的优越性，为有效改善高频地波雷达的目标探测性能提供了一种新的思路与方法。

Short-term Prediction of Ionospheric Clutter from High Frequency Surface Wave Radar Using OARO-GRU

Accurate prediction of ionospheric clutter is of great significance in improving the target detection performance of high-frequency surface wave radar. This paper proposes a short-term prediction model of ionospheric clutter using the Opposite Artificial Rabbits Optimization optimized Gated Recurrent Unit (OARO-GRU) network. Firstly, based on the a priori knowledge that ionospheric clutter received by high-frequency surface wave radar has chaotic characteristics, the input and output sample sets of the GRU network are constructed using the phase space reconstruction technique. Then, two improvement strategies, namely, the opposition-based learning and the Cauchy-based mutation, are incorporated to enhance the optimization capability of the original ARO, which is used to optimizthe GRU network with the values of three hyperparameters including the number of hidden layer nodes, the initial learning rate, and the maximum number of iterations. Finally, the optimized GRU network is retrained and fed into the test sample set for testing. The model is evaluated based on the given evaluation metrics. The experimental results show that compared with the other seven comparison forecast models, the proposed OARO-GRU network model has obvious superiority in prediction accuracy and reliability, and provides a new idea and method for effectively improving the target detection performance of high-frequency surface wave radar.