【摘要】 针对现有雷达箔条干扰识别算法存在的泛化能力不强、依赖人工特征提取和先验知识等问题，提出了基于深度神经网络的空中目标与箔条的识别算法。采用以时间卷积网络为主干的深度神经网络设计模型，对空中目标和箔条雷达回波的不同特征进行学习，从而对空中目标进行识别。采用实测雷达回波数据进行验证，结果表明，基于时间神经卷积网络的空中目标与箔条识别算法识别性能较好。该算法为雷达抗箔条干扰的研究提供了新的思路。更多还原

【Abstract】 Aiming at the problems of the low generalization ability,the reliance on the artificial feature extraction and prior knowledge in the recognition algorithms of anti-chaff interference for radar equipment, a recognition algorithm of aerial targets and chaff clouds based on deep neural network was put forward. The deep neural network with temporal convolutional network(TCN) as backbone was utilized to design the model, to learn the different features between aerial targets and chaff clouds in radar echo and recognize the aerial targets. The verification results on the measured radar echo data indicate that the recognition algorithm performance of aerial targets and chaff clouds based on deep neural network is relatively significant, which provides a new idea for the research on radar anti-chaff interference.更多还原