提出了一种解决复杂电磁环境下目标检测后仍包含杂波的基于反向传播(BP)神经网络的雷达点迹分类方法。该方法可以在目标检测后进一步区分目标点和杂波点,提高目标跟踪的质量。同时,对BP神经网络进行了训练,并与K最近邻域法和支持向量机作了对比,发现该方法的分类精度可达87. 3%，较后两种方法精度分别提升19. 6%和7. 6%。实验结果表明:基于BP神经网络的雷达点迹分类方法有效。

A radar plots classification method based on back propagation (BP) neural network is proposed to solve the problem of a lot of clutter after target detection in complex electromagnetic environment of radar detection. This method can further distinguish target points and clutter points after target detection, and improve the quality of target tracking. In this paper, BP neural network is trained and compared with K nearest neighbor method and support vector machine, it is found that the classification accuracy of this method is up to 87. 3% , which is 19. 6% and 7. 6% higher than latter two methods. The experimental results show that the radar plots classification method based on BP neural network is effective.

TN957.52