高频地波雷达海杂波神经网络选择集成预测

针对高频地波雷达海杂波的混沌特性，提出了一种新的海杂波神经网络集成预测方法。该方法在重构的相空间中估计所有神经网络在预测样本邻域内的预测性能和错误关联，进行神经网络的自适应选择和动态集成，实现海杂波的神经网络集成预测。在OSMAR2000高频地波雷达系统海洋目标实测数据上的测试结果表明,该方法具有较高的海杂波预测精度。

High frequency ground wave radar sea clutter predicting based on artifical neural network selection and ensembling

Because sea clutter in high frequency ground wave radar (HFGWR) echoes is chaotic,a new neural network ensembling method for sea clutter predicting is presented.In phase reconstructed space,the pre-dicting performance and error correlation of all artificial neural networks in the neighborhood of the current sea clutter sample are evaluated so as to adaptively choose and dynamically integrate the gualified part among them for sea clutter predicting.The testing result on the objects detecting echo data of the OSMAR2000 HFGWR system shows the predicting precision of the proposed method is higher than the non-ensembling prediction method,so it can be used to improve the objects detecting performance of HFGWR.